Systems and methods for location sensor-based branch prediction

ABSTRACT

Provided are systems and methods for location sensor-based branch prediction. In one aspect, the method includes determining a first orientation of an instrument based on first location data generated by a set of one or more location sensors for the instrument and determining a second orientation of the instrument at a second time based on second location data. A distal end of the instrument is located within a first segment of a model at the first time and the second time and the first segment branches into two or more child segments. The method also includes determining data indicative of a difference between the first orientation and the second orientation and determining a prediction that the instrument will advance into a first one of the child segments based on the data indicative of the difference.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/137,952, filed Dec. 30, 2020, which is a continuation of U.S. patentapplication Ser. No. 16/424,165, filed May 28, 2019, now U.S. Pat. No.10,905,499, which claims the benefit of U.S. Provisional Application No.62/678,160, filed May 30, 2018, and the benefit of U.S. ProvisionalApplication No. 62/678,962, filed May 31, 2018, each of which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

The systems and methods disclosed herein are directed to branchprediction in a luminal network, and more particularly to techniques forpredicting which branch an instrument will be advanced into based onlocation sensor data.

BACKGROUND

Medical procedures such as endoscopy (e.g., bronchoscopy) may involvethe insertion of a medical tool into a patient's luminal network (e.g.,airways) for diagnostic and/or therapeutic purposes. Surgical roboticsystems may be used to control the insertion and/or manipulation of themedical tool during a medical procedure. The surgical robotic system maycomprise at least one robotic arm including a manipulator assembly whichmay be used to control the positioning of the medical tool prior to andduring the medical procedure. The surgical robotic system may furthercomprise location sensor(s) configured to generate location dataindicative of a position of the distal end of the medical tool.

The surgical robotic system may further comprise one or more displaysfor providing an indication of the location of the distal end of theinstrument to a user and thereby aid the user in navigating theinstrument through the patient's luminal network. The system may beconfigured to perform various techniques in support of the navigation ofthe instrument, including predicting into which branch of the luminalnetwork the instrument is most likely to be advanced from a currentbranch.

SUMMARY

The systems, methods and devices of this disclosure each have severalinnovative aspects, no single one of which is solely responsible for thedesirable attributes disclosed herein.

In one aspect, there is provided a system, comprising a processor and atleast one computer-readable memory in communication with the processorand having stored thereon a model of a luminal network of a patient, thememory further having stored thereon computer-executable instructions tocause the processor to: determine a first orientation of an instrumentbased on first location data generated by a set of one or more locationsensors for the instrument, the first location data being indicative ofthe location of the instrument in a location sensor coordinate system ata first time; determine a second orientation of the instrument at asecond time based on second location data generated by the set oflocation sensors, a distal end of the instrument being located within afirst segment of the model at the first time and the second time and thefirst segment branching into two or more child segments; determine dataindicative of a difference between the first orientation and the secondorientation; and determine a prediction that the instrument will advanceinto a first one of the child segments based on the data indicative ofthe difference.

In another aspect, there is provided a non-transitory computer readablestorage medium having stored thereon instructions that, when executed,cause at least one computing device to: determine a first orientation ofan instrument based on first location data generated by a set of one ormore location sensors for the instrument, the first location data beingindicative of the location of the instrument in a location sensorcoordinate system at a first time; determine a second orientation of theinstrument at a second time based on second location data generated bythe set of location sensors, a distal end of the instrument beinglocated within a first segment of a model at the first time and thesecond time and the first segment branching into two or more childsegments, the model being stored in a memory and modelling a luminalnetwork of a patient; determine data indicative of a difference betweenthe first orientation and the second orientation; and determine aprediction that the instrument will advance into a first one of thechild segments based on the data indicative of the difference.

In yet another aspect, there is provided method of predicting movementof an instrument, comprising: determining a first orientation of aninstrument based on first location data generated by a set of one ormore location sensors for the instrument, the first location data beingindicative of the location of the instrument in a location sensorcoordinate system at a first time; determining a second orientation ofthe instrument at a second time based on second location data generatedby the set of location sensors, a distal end of the instrument beinglocated within a first segment of a model at the first time and thesecond time and the first segment branching into two or more childsegments, the model being stored in a memory and modelling a luminalnetwork of a patient; determining data indicative of a differencebetween the first orientation and the second orientation; anddetermining a prediction that the instrument will advance into a firstone of the child segments based on the data indicative of thedifference.

In still yet another aspect, there is provided a system, comprising aprocessor and at least one computer-readable memory in communicationwith the processor and having stored thereon a model of a luminalnetwork of a patient, the memory further having stored thereoncomputer-executable instructions to cause the processor to: determine anorientation of an instrument with respect to the model based on locationdata generated by a set of one or more location sensors for theinstrument, a distal end of the instrument being located within a firstsegment of the model and the first segment branching into two or morechild segments; determine an orientation a first one of the childsegments; and determine a prediction that the instrument will advanceinto the first child segment based on the orientation of the instrumentand the orientation of the first child segment.

In another aspect, there is provided non-transitory computer readablestorage medium having stored thereon instructions that, when executed,cause at least one computing device to: determine an orientation of aninstrument with respect to a model based on location data generated by aset of one or more location sensors for the instrument, the model beingstored in a memory and modelling a luminal network of a patient, adistal end of the instrument being located within a first segment of themodel and the first segment branching into two or more child segments;determine an orientation a first one of the child segments; anddetermine a prediction that the instrument will advance into the firstchild segment based on the orientation of the instrument and theorientation of the first child segment.

In yet another aspect, there is provided a method of predicting movementof an instrument, comprising: determining an orientation of aninstrument with respect to a model based on location data generated by aset of one or more location sensors for the instrument, the model beingstored in a memory and modelling a luminal network of a patient, adistal end of the instrument being located within a first segment of themodel and the first segment branching into two or more child segments;determining an orientation a first one of the child segments; anddetermining a prediction that the instrument will advance into the firstchild segment based on the orientation of the instrument and theorientation of the first child segment.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed aspects will hereinafter be described in conjunction withthe appended drawings, provided to illustrate and not to limit thedisclosed aspects, wherein like designations denote like elements.

FIG. 1 illustrates an embodiment of a cart-based robotic system arrangedfor diagnostic and/or therapeutic bronchoscopy procedure(s).

FIG. 2 depicts further aspects of the robotic system of FIG. 1 .

FIG. 3 illustrates an embodiment of the robotic system of FIG. 1arranged for ureteroscopy.

FIG. 4 illustrates an embodiment of the robotic system of FIG. 1arranged for a vascular procedure.

FIG. 5 illustrates an embodiment of a table-based robotic systemarranged for a bronchoscopy procedure.

FIG. 6 provides an alternative view of the robotic system of FIG. 5 .

FIG. 7 illustrates an example system configured to stow robotic arm(s).

FIG. 8 illustrates an embodiment of a table-based robotic systemconfigured for a ureteroscopy procedure.

FIG. 9 illustrates an embodiment of a table-based robotic systemconfigured for a laparoscopic procedure.

FIG. 10 illustrates an embodiment of the table-based robotic system ofFIGS. 5-9 with pitch or tilt adjustment.

FIG. 11 provides a detailed illustration of the interface between thetable and the column of the table-based robotic system of FIGS. 5-10 .

FIG. 12 illustrates an exemplary instrument driver.

FIG. 13 illustrates an exemplary medical instrument with a pairedinstrument driver.

FIG. 14 illustrates an alternative design for an instrument driver andinstrument where the axes of the drive units are parallel to the axis ofthe elongated shaft of the instrument.

FIG. 15 depicts a block diagram illustrating a localization system thatestimates a location of one or more elements of the robotic systems ofFIGS. 1-10 , such as the location of the instrument of FIGS. 13 and 14 ,in accordance to an example embodiment.

FIG. 16A illustrates an example operating environment implementing oneor more aspects of the disclosed branch prediction systems andtechniques.

FIG. 16B illustrates an example luminal network that can be navigated inthe operating environment of FIG. 16A.

FIG. 16C illustrates an example command console that can be used, forexample, as the command console in the example operating environment.

FIG. 17A provides an overview of an example block diagram of thenavigation configuration system, according to one embodiment.

FIG. 17B shows an example block diagram of the navigation module shownin FIG. 17A, according to one embodiment.

FIG. 17C shows an example block diagram of the estimated state datastore included in the state estimator, according to one embodiment.

FIG. 17D illustrates an example location sensor-based branch predictionsystem in accordance with aspects of this disclosure.

FIG. 18A is a flowchart illustrating an example method operable by arobotic system, or component(s) thereof, for unregistered locationsensor-based branch prediction in accordance with aspects of thisdisclosure.

FIG. 18B illustrates an example set of location data points which may begenerated by one or more location sensors as an instrument is driventhrough a luminal network in accordance with aspects of this disclosure.

FIG. 19 illustrates an example luminal network in which locationsensor-based branch prediction can be performed in accordance withaspects of this disclosure.

FIG. 20 is a flowchart illustrating an example method operable by arobotic system, or component(s) thereof, for registered locationsensor-based branch prediction in accordance with aspects of thisdisclosure.

FIG. 21 is a flowchart illustrating an example method operable by arobotic system, or component(s) thereof, for location sensor-basedbranch prediction in accordance with aspects of this disclosure.

DETAILED DESCRIPTION 1. Overview.

Aspects of the present disclosure may be integrated into arobotically-enabled medical system capable of performing a variety ofmedical procedures, including both minimally invasive, such aslaparoscopy, and non-invasive, such as endoscopy, procedures. Amongendoscopy procedures, the system may be capable of performingbronchoscopy, ureteroscopy, gastroscopy, etc.

In addition to performing the breadth of procedures, the system mayprovide additional benefits, such as enhanced imaging and guidance toassist the physician. Additionally, the system may provide the physicianwith the ability to perform the procedure from an ergonomic positionwithout the need for awkward arm motions and positions. Still further,the system may provide the physician with the ability to perform theprocedure with improved ease of use such that one or more of theinstruments of the system can be controlled by a single user.

Various embodiments will be described below in conjunction with thedrawings for purposes of illustration. It should be appreciated thatmany other implementations of the disclosed concepts are possible, andvarious advantages can be achieved with the disclosed implementations.Headings are included herein for reference and to aid in locatingvarious sections. These headings are not intended to limit the scope ofthe concepts described with respect thereto. Such concepts may haveapplicability throughout the entire specification.

A. Robotic System—Cart.

The robotically-enabled medical system may be configured in a variety ofways depending on the particular procedure. FIG. 1 illustrates anembodiment of a cart-based robotically-enabled system 10 arranged for adiagnostic and/or therapeutic bronchoscopy procedure. During abronchoscopy, the system 10 may comprise a cart 11 having one or morerobotic arms 12 to deliver a medical instrument, such as a steerableendoscope 13, which may be a procedure-specific bronchoscope forbronchoscopy, to a natural orifice access point (i.e., the mouth of thepatient positioned on a table in the present example) to deliverdiagnostic and/or therapeutic tools. As shown, the cart 11 may bepositioned proximate to the patient's upper torso in order to provideaccess to the access point. Similarly, the robotic arms 12 may beactuated to position the bronchoscope relative to the access point. Thearrangement in FIG. 1 may also be utilized when performing agastro-intestinal (GI) procedure with a gastroscope, a specializedendoscope for GI procedures. FIG. 2 depicts an example embodiment of thecart in greater detail.

With continued reference to FIG. 1 , once the cart 11 is properlypositioned, the robotic arms 12 may insert the steerable endoscope 13into the patient robotically, manually, or a combination thereof. Asshown, the steerable endoscope 13 may comprise at least two telescopingparts, such as an inner leader portion and an outer sheath portion, eachportion coupled to a separate instrument driver from the set ofinstrument drivers 28, each instrument driver coupled to the distal endof an individual robotic arm. This linear arrangement of the instrumentdrivers 28, which facilitates coaxially aligning the leader portion withthe sheath portion, creates a “virtual rail” 29 that may be repositionedin space by manipulating the one or more robotic arms 12 into differentangles and/or positions. The virtual rails described herein are depictedin the Figures using dashed lines, and accordingly the dashed lines donot depict any physical structure of the system. Translation of theinstrument drivers 28 along the virtual rail 29 telescopes the innerleader portion relative to the outer sheath portion or advances orretracts the endoscope 13 from the patient. The angle of the virtualrail 29 may be adjusted, translated, and pivoted based on clinicalapplication or physician preference. For example, in bronchoscopy, theangle and position of the virtual rail 29 as shown represents acompromise between providing physician access to the endoscope 13 whileminimizing friction that results from bending the endoscope 13 into thepatient's mouth.

The endoscope 13 may be directed down the patient's trachea and lungsafter insertion using precise commands from the robotic system untilreaching the target destination or operative site. In order to enhancenavigation through the patient's lung network and/or reach the desiredtarget, the endoscope 13 may be manipulated to telescopically extend theinner leader portion from the outer sheath portion to obtain enhancedarticulation and greater bend radius. The use of separate instrumentdrivers 28 also allows the leader portion and sheath portion to bedriven independent of each other.

For example, the endoscope 13 may be directed to deliver a biopsy needleto a target, such as, for example, a lesion or nodule within the lungsof a patient. The needle may be deployed down a working channel thatruns the length of the endoscope to obtain a tissue sample to beanalyzed by a pathologist. Depending on the pathology results,additional tools may be deployed down the working channel of theendoscope for additional biopsies. After identifying a nodule to bemalignant, the endoscope 13 may endoscopically deliver tools to resectthe potentially cancerous tissue. In some instances, diagnostic andtherapeutic treatments may need to be delivered in separate procedures.In those circumstances, the endoscope 13 may also be used to deliver afiducial to “mark” the location of the target nodule as well. In otherinstances, diagnostic and therapeutic treatments may be delivered duringthe same procedure.

The system 10 may also include a movable tower 30, which may beconnected via support cables to the cart 11 to provide support forcontrols, electronics, fluidics, optics, sensors, and/or power to thecart 11. Placing such functionality in the tower 30 allows for a smallerform factor cart 11 that may be more easily adjusted and/orre-positioned by an operating physician and his/her staff. Additionally,the division of functionality between the cart/table and the supporttower 30 reduces operating room clutter and facilitates improvingclinical workflow. While the cart 11 may be positioned close to thepatient, the tower 30 may be stowed in a remote location to stay out ofthe way during a procedure.

In support of the robotic systems described above, the tower 30 mayinclude component(s) of a computer-based control system that storescomputer program instructions, for example, within a non-transitorycomputer-readable storage medium such as a persistent magnetic storagedrive, solid state drive, etc. The execution of those instructions,whether the execution occurs in the tower 30 or the cart 11, may controlthe entire system or sub-system(s) thereof. For example, when executedby a processor of the computer system, the instructions may cause thecomponents of the robotics system to actuate the relevant carriages andarm mounts, actuate the robotics arms, and control the medicalinstruments. For example, in response to receiving the control signal,the motors in the joints of the robotics arms may position the arms intoa certain posture.

The tower 30 may also include a pump, flow meter, valve control, and/orfluid access in order to provide controlled irrigation and aspirationcapabilities to system that may be deployed through the endoscope 13.These components may also be controlled using the computer system oftower 30. In some embodiments, irrigation and aspiration capabilitiesmay be delivered directly to the endoscope 13 through separate cable(s).

The tower 30 may include a voltage and surge protector designed toprovide filtered and protected electrical power to the cart 11, therebyavoiding placement of a power transformer and other auxiliary powercomponents in the cart 11, resulting in a smaller, more moveable cart11.

The tower 30 may also include support equipment for the sensors deployedthroughout the robotic system 10. For example, the tower 30 may includeopto-electronics equipment for detecting, receiving, and processing datareceived from the optical sensors or cameras throughout the roboticsystem 10. In combination with the control system, such opto-electronicsequipment may be used to generate real-time images for display in anynumber of consoles deployed throughout the system, including in thetower 30. Similarly, the tower 30 may also include an electronicsubsystem for receiving and processing signals received from deployedelectromagnetic (EM) sensors. The tower 30 may also be used to house andposition an EM field generator for detection by EM sensors in or on themedical instrument.

The tower 30 may also include a console 31 in addition to other consolesavailable in the rest of the system, e.g., console mounted on top of thecart. The console 31 may include a user interface and a display screen,such as a touchscreen, for the physician operator. Consoles in system 10are generally designed to provide both robotic controls as well aspre-operative and real-time information of the procedure, such asnavigational and localization information of the endoscope 13. When theconsole 31 is not the only console available to the physician, it may beused by a second operator, such as a nurse, to monitor the health orvitals of the patient and the operation of system, as well as provideprocedure-specific data, such as navigational and localizationinformation.

The tower 30 may be coupled to the cart 11 and endoscope 13 through oneor more cables or connections (not shown). In some embodiments, thesupport functionality from the tower 30 may be provided through a singlecable to the cart 11, simplifying and de-cluttering the operating room.In other embodiments, specific functionality may be coupled in separatecabling and connections. For example, while power may be providedthrough a single power cable to the cart, the support for controls,optics, fluidics, and/or navigation may be provided through a separatecable.

FIG. 2 provides a detailed illustration of an embodiment of the cartfrom the cart-based robotically-enabled system shown in FIG. 1 . Thecart 11 generally includes an elongated support structure 14 (oftenreferred to as a “column”), a cart base 15, and a console 16 at the topof the column 14. The column 14 may include one or more carriages, suchas a carriage 17 (alternatively “arm support”) for supporting thedeployment of one or more robotic arms 12 (three shown in FIG. 2 ). Thecarriage 17 may include individually configurable arm mounts that rotatealong a perpendicular axis to adjust the base of the robotic arms 12 forbetter positioning relative to the patient. The carriage 17 alsoincludes a carriage interface 19 that allows the carriage 17 tovertically translate along the column 14.

The carriage interface 19 is connected to the column 14 through slots,such as slot 20, that are positioned on opposite sides of the column 14to guide the vertical translation of the carriage 17. The slot 20contains a vertical translation interface to position and hold thecarriage at various vertical heights relative to the cart base 15.Vertical translation of the carriage 17 allows the cart 11 to adjust thereach of the robotic arms 12 to meet a variety of table heights, patientsizes, and physician preferences. Similarly, the individuallyconfigurable arm mounts on the carriage 17 allow the robotic arm base 21of robotic arms 12 to be angled in a variety of configurations.

In some embodiments, the slot 20 may be supplemented with slot coversthat are flush and parallel to the slot surface to prevent dirt andfluid ingress into the internal chambers of the column 14 and thevertical translation interface as the carriage 17 vertically translates.The slot covers may be deployed through pairs of spring spoolspositioned near the vertical top and bottom of the slot 20. The coversare coiled within the spools until deployed to extend and retract fromtheir coiled state as the carriage 17 vertically translates up and down.The spring-loading of the spools provides force to retract the coverinto a spool when carriage 17 translates towards the spool, while alsomaintaining a tight seal when the carriage 17 translates away from thespool. The covers may be connected to the carriage 17 using, forexample, brackets in the carriage interface 19 to ensure properextension and retraction of the cover as the carriage 17 translates.

The column 14 may internally comprise mechanisms, such as gears andmotors, that are designed to use a vertically aligned lead screw totranslate the carriage 17 in a mechanized fashion in response to controlsignals generated in response to user inputs, e.g., inputs from theconsole 16.

The robotic arms 12 may generally comprise robotic arm bases 21 and endeffectors 22, separated by a series of linkages 23 that are connected bya series of joints 24, each joint comprising an independent actuator,each actuator comprising an independently controllable motor. Eachindependently controllable joint represents an independent degree offreedom available to the robotic arm. Each of the arms 12 have sevenjoints, and thus provide seven degrees of freedom. A multitude of jointsresult in a multitude of degrees of freedom, allowing for “redundant”degrees of freedom. Redundant degrees of freedom allow the robotic arms12 to position their respective end effectors 22 at a specific position,orientation, and trajectory in space using different linkage positionsand joint angles. This allows for the system to position and direct amedical instrument from a desired point in space while allowing thephysician to move the arm joints into a clinically advantageous positionaway from the patient to create greater access, while avoiding armcollisions.

The cart base 15 balances the weight of the column 14, carriage 17, andarms 12 over the floor. Accordingly, the cart base 15 houses heaviercomponents, such as electronics, motors, power supply, as well ascomponents that either enable movement and/or immobilize the cart. Forexample, the cart base 15 includes rollable wheel-shaped casters 25 thatallow for the cart to easily move around the room prior to a procedure.After reaching the appropriate position, the casters 25 may beimmobilized using wheel locks to hold the cart 11 in place during theprocedure.

Positioned at the vertical end of column 14, the console 16 allows forboth a user interface for receiving user input and a display screen (ora dual-purpose device such as, for example, a touchscreen 26) to providethe physician user with both pre-operative and intra-operative data.Potential pre-operative data on the touchscreen 26 may includepre-operative plans, navigation and mapping data derived frompre-operative computerized tomography (CT) scans, and/or notes frompre-operative patient interviews. Intra-operative data on display mayinclude optical information provided from the tool, sensor andcoordinate information from sensors, as well as vital patientstatistics, such as respiration, heart rate, and/or pulse. The console16 may be positioned and tilted to allow a physician to access theconsole from the side of the column 14 opposite carriage 17. From thisposition, the physician may view the console 16, robotic arms 12, andpatient while operating the console 16 from behind the cart 11. Asshown, the console 16 also includes a handle 27 to assist withmaneuvering and stabilizing cart 11.

FIG. 3 illustrates an embodiment of a robotically-enabled system 10arranged for ureteroscopy. In a ureteroscopic procedure, the cart 11 maybe positioned to deliver a ureteroscope 32, a procedure-specificendoscope designed to traverse a patient's urethra and ureter, to thelower abdominal area of the patient. In a ureteroscopy, it may bedesirable for the ureteroscope 32 to be directly aligned with thepatient's urethra to reduce friction and forces on the sensitive anatomyin the area. As shown, the cart 11 may be aligned at the foot of thetable to allow the robotic arms 12 to position the ureteroscope 32 fordirect linear access to the patient's urethra. From the foot of thetable, the robotic arms 12 may insert the ureteroscope 32 along thevirtual rail 33 directly into the patient's lower abdomen through theurethra.

After insertion into the urethra, using similar control techniques as inbronchoscopy, the ureteroscope 32 may be navigated into the bladder,ureters, and/or kidneys for diagnostic and/or therapeutic applications.For example, the ureteroscope 32 may be directed into the ureter andkidneys to break up kidney stone build up using laser or ultrasoniclithotripsy device deployed down the working channel of the ureteroscope32. After lithotripsy is complete, the resulting stone fragments may beremoved using baskets deployed down the ureteroscope 32.

FIG. 4 illustrates an embodiment of a robotically-enabled systemsimilarly arranged for a vascular procedure. In a vascular procedure,the system 10 may be configured such the cart 11 may deliver a medicalinstrument 34, such as a steerable catheter, to an access point in thefemoral artery in the patient's leg. The femoral artery presents both alarger diameter for navigation as well as relatively less circuitous andtortuous path to the patient's heart, which simplifies navigation. As ina ureteroscopic procedure, the cart 11 may be positioned towards thepatient's legs and lower abdomen to allow the robotic arms 12 to providea virtual rail 35 with direct linear access to the femoral artery accesspoint in the patient's thigh/hip region. After insertion into theartery, the medical instrument 34 may be directed and inserted bytranslating the instrument drivers 28. Alternatively, the cart may bepositioned around the patient's upper abdomen in order to reachalternative vascular access points, such as, for example, the carotidand brachial arteries near the shoulder and wrist.

B. Robotic System—Table.

Embodiments of the robotically-enabled medical system may alsoincorporate the patient's table. Incorporation of the table reduces theamount of capital equipment within the operating room by removing thecart, which allows greater access to the patient. FIG. 5 illustrates anembodiment of such a robotically-enabled system arranged for abronchoscopy procedure. System 36 includes a support structure or column37 for supporting platform 38 (shown as a “table” or “bed”) over thefloor. Much like in the cart-based systems, the end effectors of therobotic arms 39 of the system 36 comprise instrument drivers 42 that aredesigned to manipulate an elongated medical instrument, such as abronchoscope 40 in FIG. 5 , through or along a virtual rail 41 formedfrom the linear alignment of the instrument drivers 42. In practice, aC-arm for providing fluoroscopic imaging may be positioned over thepatient's upper abdominal area by placing the emitter and detectoraround table 38.

FIG. 6 provides an alternative view of the system 36 without the patientand medical instrument for discussion purposes. As shown, the column 37may include one or more carriages 43 shown as ring-shaped in the system36, from which the one or more robotic arms 39 may be based. Thecarriages 43 may translate along a vertical column interface 44 thatruns the length of the column 37 to provide different vantage pointsfrom which the robotic arms 39 may be positioned to reach the patient.The carriage(s) 43 may rotate around the column 37 using a mechanicalmotor positioned within the column 37 to allow the robotic arms 39 tohave access to multiples sides of the table 38, such as, for example,both sides of the patient. In embodiments with multiple carriages, thecarriages may be individually positioned on the column and may translateand/or rotate independent of the other carriages. While carriages 43need not surround the column 37 or even be circular, the ring-shape asshown facilitates rotation of the carriages 43 around the column 37while maintaining structural balance. Rotation and translation of thecarriages 43 allows the system to align the medical instruments, such asendoscopes and laparoscopes, into different access points on thepatient.

The arms 39 may be mounted on the carriages through a set of arm mounts45 comprising a series of joints that may individually rotate and/ortelescopically extend to provide additional configurability to therobotic arms 39. Additionally, the arm mounts 45 may be positioned onthe carriages 43 such that, when the carriages 43 are appropriatelyrotated, the arm mounts 45 may be positioned on either the same side oftable 38 (as shown in FIG. 6 ), on opposite sides of table 38 (as shownin FIG. 9 ), or on adjacent sides of the table 38 (not shown).

The column 37 structurally provides support for the table 38, and a pathfor vertical translation of the carriages. Internally, the column 37 maybe equipped with lead screws for guiding vertical translation of thecarriages, and motors to mechanize the translation of said carriagesbased the lead screws. The column 37 may also convey power and controlsignals to the carriage 43 and robotic arms 39 mounted thereon.

The table base 46 serves a similar function as the cart base 15 in cart11 shown in FIG. 2 , housing heavier components to balance the table/bed38, the column 37, the carriages 43, and the robotic arms 39. The tablebase 46 may also incorporate rigid casters to provide stability duringprocedures. Deployed from the bottom of the table base 46, the castersmay extend in opposite directions on both sides of the base 46 andretract when the system 36 needs to be moved.

Continuing with FIG. 6 , the system 36 may also include a tower (notshown) that divides the functionality of system 36 between table andtower to reduce the form factor and bulk of the table. As in earlierdisclosed embodiments, the tower may provide a variety of supportfunctionalities to table, such as processing, computing, and controlcapabilities, power, fluidics, and/or optical and sensor processing. Thetower may also be movable to be positioned away from the patient toimprove physician access and de-clutter the operating room.Additionally, placing components in the tower allows for more storagespace in the table base for potential stowage of the robotic arms. Thetower may also include a console that provides both a user interface foruser input, such as keyboard and/or pendant, as well as a display screen(or touchscreen) for pre-operative and intra-operative information, suchas real-time imaging, navigation, and tracking information.

In some embodiments, a table base may stow and store the robotic armswhen not in use. FIG. 7 illustrates a system 47 that stows robotic armsin an embodiment of the table-based system. In system 47, carriages 48may be vertically translated into base 49 to stow robotic arms 50, armmounts 51, and the carriages 48 within the base 49. Base covers 52 maybe translated and retracted open to deploy the carriages 48, arm mounts51, and arms 50 around column 53, and closed to stow to protect themwhen not in use. The base covers 52 may be sealed with a membrane 54along the edges of its opening to prevent dirt and fluid ingress whenclosed.

FIG. 8 illustrates an embodiment of a robotically-enabled table-basedsystem configured for a ureteroscopy procedure. In a ureteroscopy, thetable 38 may include a swivel portion 55 for positioning a patientoff-angle from the column 37 and table base 46. The swivel portion 55may rotate or pivot around a pivot point (e.g., located below thepatient's head) in order to position the bottom portion of the swivelportion 55 away from the column 37. For example, the pivoting of theswivel portion 55 allows a C-arm (not shown) to be positioned over thepatient's lower abdomen without competing for space with the column (notshown) below table 38. By rotating the carriage 35 (not shown) aroundthe column 37, the robotic arms 39 may directly insert a ureteroscope 56along a virtual rail 57 into the patient's groin area to reach theurethra. In a ureteroscopy, stirrups 58 may also be fixed to the swivelportion 55 of the table 38 to support the position of the patient's legsduring the procedure and allow clear access to the patient's groin area.

In a laparoscopic procedure, through small incision(s) in the patient'sabdominal wall, minimally invasive instruments (elongated in shape toaccommodate the size of the one or more incisions) may be inserted intothe patient's anatomy. After inflation of the patient's abdominalcavity, the instruments, often referred to as laparoscopes, may bedirected to perform surgical tasks, such as grasping, cutting, ablating,suturing, etc. FIG. 9 illustrates an embodiment of a robotically-enabledtable-based system configured for a laparoscopic procedure. As shown inFIG. 9 , the carriages 43 of the system 36 may be rotated and verticallyadjusted to position pairs of the robotic arms 39 on opposite sides ofthe table 38, such that laparoscopes 59 may be positioned using the armmounts 45 to be passed through minimal incisions on both sides of thepatient to reach his/her abdominal cavity.

To accommodate laparoscopic procedures, the robotically-enabled tablesystem may also tilt the platform to a desired angle. FIG. 10illustrates an embodiment of the robotically-enabled medical system withpitch or tilt adjustment. As shown in FIG. 10 , the system 36 mayaccommodate tilt of the table 38 to position one portion of the table ata greater distance from the floor than the other. Additionally, the armmounts 45 may rotate to match the tilt such that the arms 39 maintainthe same planar relationship with table 38. To accommodate steeperangles, the column 37 may also include telescoping portions 60 thatallow vertical extension of column 37 to keep the table 38 from touchingthe floor or colliding with base 46.

FIG. 11 provides a detailed illustration of the interface between thetable 38 and the column 37. Pitch rotation mechanism 61 may beconfigured to alter the pitch angle of the table 38 relative to thecolumn 37 in multiple degrees of freedom. The pitch rotation mechanism61 may be enabled by the positioning of orthogonal axes 1, 2 at thecolumn-table interface, each axis actuated by a separate motor 3, 4responsive to an electrical pitch angle command. Rotation along onescrew 5 would enable tilt adjustments in one axis 1, while rotationalong the other screw 6 would enable tilt adjustments along the otheraxis 2.

For example, pitch adjustments are particularly useful when trying toposition the table in a Trendelenburg position, i.e., position thepatient's lower abdomen at a higher position from the floor than thepatient's lower abdomen, for lower abdominal surgery. The Trendelenburgposition causes the patient's internal organs to slide towards his/herupper abdomen through the force of gravity, clearing out the abdominalcavity for minimally invasive tools to enter and perform lower abdominalsurgical procedures, such as laparoscopic prostatectomy.

C. Instrument Driver & Interface.

The end effectors of the system's robotic arms comprise (i) aninstrument driver (alternatively referred to as “instrument drivemechanism” or “instrument device manipulator”) that incorporateelectro-mechanical means for actuating the medical instrument and (ii) aremovable or detachable medical instrument which may be devoid of anyelectro-mechanical components, such as motors. This dichotomy may bedriven by the need to sterilize medical instruments used in medicalprocedures, and the inability to adequately sterilize expensive capitalequipment due to their intricate mechanical assemblies and sensitiveelectronics. Accordingly, the medical instruments may be designed to bedetached, removed, and interchanged from the instrument driver (and thusthe system) for individual sterilization or disposal by the physician orthe physician's staff. In contrast, the instrument drivers need not bechanged or sterilized, and may be draped for protection.

FIG. 12 illustrates an example instrument driver. Positioned at thedistal end of a robotic arm, instrument driver 62 comprises of one ormore drive units 63 arranged with parallel axes to provide controlledtorque to a medical instrument via drive shafts 64. Each drive unit 63comprises an individual drive shaft 64 for interacting with theinstrument, a gear head 65 for converting the motor shaft rotation to adesired torque, a motor 66 for generating the drive torque, an encoder67 to measure the speed of the motor shaft and provide feedback to thecontrol circuitry, and control circuitry 68 for receiving controlsignals and actuating the drive unit. Each drive unit 63 beingindependent controlled and motorized, the instrument driver 62 mayprovide multiple (four as shown in FIG. 12 ) independent drive outputsto the medical instrument. In operation, the control circuitry 68 wouldreceive a control signal, transmit a motor signal to the motor 66,compare the resulting motor speed as measured by the encoder 67 with thedesired speed, and modulate the motor signal to generate the desiredtorque.

For procedures that require a sterile environment, the robotic systemmay incorporate a drive interface, such as a sterile adapter connectedto a sterile drape, that sits between the instrument driver and themedical instrument. The chief purpose of the sterile adapter is totransfer angular motion from the drive shafts of the instrument driverto the drive inputs of the instrument while maintaining physicalseparation, and thus sterility, between the drive shafts and driveinputs. Accordingly, an example sterile adapter may comprise of a seriesof rotational inputs and outputs intended to be mated with the driveshafts of the instrument driver and drive inputs on the instrument.Connected to the sterile adapter, the sterile drape, comprised of athin, flexible material such as transparent or translucent plastic, isdesigned to cover the capital equipment, such as the instrument driver,robotic arm, and cart (in a cart-based system) or table (in atable-based system). Use of the drape would allow the capital equipmentto be positioned proximate to the patient while still being located inan area not requiring sterilization (i.e., non-sterile field). On theother side of the sterile drape, the medical instrument may interfacewith the patient in an area requiring sterilization (i.e., sterilefield).

D. Medical Instrument.

FIG. 13 illustrates an example medical instrument with a pairedinstrument driver. Like other instruments designed for use with arobotic system, medical instrument 70 comprises an elongated shaft 71(or elongate body) and an instrument base 72. The instrument base 72,also referred to as an “instrument handle” due to its intended designfor manual interaction by the physician, may generally compriserotatable drive inputs 73, e.g., receptacles, pulleys or spools, thatare designed to be mated with drive outputs 74 that extend through adrive interface on instrument driver 75 at the distal end of robotic arm76. When physically connected, latched, and/or coupled, the mated driveinputs 73 of instrument base 72 may share axes of rotation with thedrive outputs 74 in the instrument driver 75 to allow the transfer oftorque from drive outputs 74 to drive inputs 73. In some embodiments,the drive outputs 74 may comprise splines that are designed to mate withreceptacles on the drive inputs 73.

The elongated shaft 71 is designed to be delivered through either ananatomical opening or lumen, e.g., as in endoscopy, or a minimallyinvasive incision, e.g., as in laparoscopy. The elongated shaft 66 maybe either flexible (e.g., having properties similar to an endoscope) orrigid (e.g., having properties similar to a laparoscope) or contain acustomized combination of both flexible and rigid portions. Whendesigned for laparoscopy, the distal end of a rigid elongated shaft maybe connected to an end effector comprising a jointed wrist formed from aclevis with an axis of rotation and a surgical tool, such as, forexample, a grasper or scissors, that may be actuated based on force fromthe tendons as the drive inputs rotate in response to torque receivedfrom the drive outputs 74 of the instrument driver 75. When designed forendoscopy, the distal end of a flexible elongated shaft may include asteerable or controllable bending section that may be articulated andbent based on torque received from the drive outputs 74 of theinstrument driver 75.

Torque from the instrument driver 75 is transmitted down the elongatedshaft 71 using tendons within the shaft 71. These individual tendons,such as pull wires, may be individually anchored to individual driveinputs 73 within the instrument handle 72. From the handle 72, thetendons are directed down one or more pull lumens within the elongatedshaft 71 and anchored at the distal portion of the elongated shaft 71.In laparoscopy, these tendons may be coupled to a distally mounted endeffector, such as a wrist, grasper, or scissor. Under such anarrangement, torque exerted on drive inputs 73 would transfer tension tothe tendon, thereby causing the end effector to actuate in some way. Inlaparoscopy, the tendon may cause a joint to rotate about an axis,thereby causing the end effector to move in one direction or another.Alternatively, the tendon may be connected to one or more jaws of agrasper at distal end of the elongated shaft 71, where tension from thetendon cause the grasper to close.

In endoscopy, the tendons may be coupled to a bending or articulatingsection positioned along the elongated shaft 71 (e.g., at the distalend) via adhesive, control ring, or other mechanical fixation. Whenfixedly attached to the distal end of a bending section, torque exertedon drive inputs 73 would be transmitted down the tendons, causing thesofter, bending section (sometimes referred to as the articulablesection or region) to bend or articulate. Along the non-bendingsections, it may be advantageous to spiral or helix the individual pulllumens that direct the individual tendons along (or inside) the walls ofthe endoscope shaft to balance the radial forces that result fromtension in the pull wires. The angle of the spiraling and/or spacingthere between may be altered or engineered for specific purposes,wherein tighter spiraling exhibits lesser shaft compression under loadforces, while lower amounts of spiraling results in greater shaftcompression under load forces, but also exhibits limits bending. On theother end of the spectrum, the pull lumens may be directed parallel tothe longitudinal axis of the elongated shaft 71 to allow for controlledarticulation in the desired bending or articulable sections.

In endoscopy, the elongated shaft 71 houses a number of components toassist with the robotic procedure. The shaft may comprise of a workingchannel for deploying surgical tools, irrigation, and/or aspiration tothe operative region at the distal end of the shaft 71. The shaft 71 mayalso accommodate wires and/or optical fibers to transfer signals to/froman optical assembly at the distal tip, which may include of an opticalcamera. The shaft 71 may also accommodate optical fibers to carry lightfrom proximally-located light sources, such as light emitting diodes, tothe distal end of the shaft.

At the distal end of the instrument 70, the distal tip may also comprisethe opening of a working channel for delivering tools for diagnosticand/or therapy, irrigation, and aspiration to an operative site. Thedistal tip may also include a port for a camera, such as a fiberscope ora digital camera, to capture images of an internal anatomical space.Relatedly, the distal tip may also include ports for light sources forilluminating the anatomical space when using the camera.

In the example of FIG. 13 , the drive shaft axes, and thus the driveinput axes, are orthogonal to the axis of the elongated shaft. Thisarrangement, however, complicates roll capabilities for the elongatedshaft 71. Rolling the elongated shaft 71 along its axis while keepingthe drive inputs 73 static results in undesirable tangling of thetendons as they extend off the drive inputs 73 and enter pull lumenswithin the elongate shaft 71. The resulting entanglement of such tendonsmay disrupt any control algorithms intended to predict movement of theflexible elongate shaft during an endoscopic procedure.

FIG. 14 illustrates an alternative design for an instrument driver andinstrument where the axes of the drive units are parallel to the axis ofthe elongated shaft of the instrument. As shown, a circular instrumentdriver 80 comprises four drive units with their drive outputs 81 alignedin parallel at the end of a robotic arm 82. The drive units, and theirrespective drive outputs 81, are housed in a rotational assembly 83 ofthe instrument driver 80 that is driven by one of the drive units withinthe assembly 83. In response to torque provided by the rotational driveunit, the rotational assembly 83 rotates along a circular bearing thatconnects the rotational assembly 83 to the non-rotational portion 84 ofthe instrument driver. Power and controls signals may be communicatedfrom the non-rotational portion 84 of the instrument driver 80 to therotational assembly 83 through electrical contacts may be maintainedthrough rotation by a brushed slip ring connection (not shown). In otherembodiments, the rotational assembly 83 may be responsive to a separatedrive unit that is integrated into the non-rotatable portion 84, andthus not in parallel to the other drive units. The rotational mechanism83 allows the instrument driver 80 to rotate the drive units, and theirrespective drive outputs 81, as a single unit around an instrumentdriver axis 85.

Like earlier disclosed embodiments, an instrument 86 may comprise of anelongated shaft portion 88 and an instrument base 87 (shown with atransparent external skin for discussion purposes) comprising aplurality of drive inputs 89 (such as receptacles, pulleys, and spools)that are configured to receive the drive outputs 81 in the instrumentdriver 80. Unlike prior disclosed embodiments, instrument shaft 88extends from the center of instrument base 87 with an axis substantiallyparallel to the axes of the drive inputs 89, rather than orthogonal asin the design of FIG. 13 .

When coupled to the rotational assembly 83 of the instrument driver 80,the medical instrument 86, comprising instrument base 87 and instrumentshaft 88, rotates in combination with the rotational assembly 83 aboutthe instrument driver axis 85. Since the instrument shaft 88 ispositioned at the center of instrument base 87, the instrument shaft 88is coaxial with instrument driver axis 85 when attached. Thus, rotationof the rotational assembly 83 causes the instrument shaft 88 to rotateabout its own longitudinal axis. Moreover, as the instrument base 87rotates with the instrument shaft 88, any tendons connected to the driveinputs 89 in the instrument base 87 are not tangled during rotation.Accordingly, the parallelism of the axes of the drive outputs 81, driveinputs 89, and instrument shaft 88 allows for the shaft rotation withouttangling any control tendons.

E. Navigation and Control.

Traditional endoscopy may involve the use of fluoroscopy (e.g., as maybe delivered through a C-arm) and other forms of radiation-based imagingmodalities to provide endoluminal guidance to an operator physician. Incontrast, the robotic systems contemplated by this disclosure canprovide for non-radiation-based navigational and localization means toreduce physician exposure to radiation and reduce the amount ofequipment within the operating room. As used herein, the term“localization” may refer to determining and/or monitoring the positionof objects in a reference coordinate system. Technologies such aspre-operative mapping, computer vision, real-time EM tracking, and robotcommand data may be used individually or in combination to achieve aradiation-free operating environment. In other cases, whereradiation-based imaging modalities are still used, the pre-operativemapping, computer vision, real-time EM tracking, and robot command datamay be used individually or in combination to improve upon theinformation obtained solely through radiation-based imaging modalities.

FIG. 15 is a block diagram illustrating a localization system 90 thatestimates a location of one or more elements of the robotic system, suchas the location of the instrument, in accordance to an exampleembodiment. The localization system 90 may be a set of one or morecomputer devices configured to execute one or more instructions. Thecomputer devices may be embodied by a processor (or processors) andcomputer-readable memory in one or more components discussed above. Byway of example and not limitation, the computer devices may be in thetower 30 shown in FIG. 1 , the cart shown in FIGS. 1-4 , the beds shownin FIGS. 5-10 , etc.

As shown in FIG. 15 , the localization system 90 may include alocalization module 95 that processes input data 91-94 to generatelocation data 96 for the distal tip of a medical instrument. Thelocation data 96 may be data or logic that represents a location and/ororientation of the distal end of the instrument relative to a frame ofreference. The frame of reference can be a frame of reference relativeto the anatomy of the patient or to a known object, such as an EM fieldgenerator (see discussion below for the EM field generator). Thelocation data 96 may also be referred to herein as “state data” whichdescribes a current state of the distal tip of the medical instrumentwith respect to a model (e.g., a skeletal model) of the anatomy of thepatient. The state data may include information such as a position andorientation of the distal tip of the medical instrument for a givensample period. For example, when the patient's anatomy is modeled usinga skeletal model based on a midpoint of the luminal network, theposition may take the form of a segment ID and a depth along thesegment.

The various input data 91-94 are now described in greater detail.Pre-operative mapping may be accomplished through the use of thecollection of low dose CT scans. Pre-operative CT scans arereconstructed into three-dimensional (3D) images, which are visualized,e.g., as “slices” of a cutaway view of the patient's internal anatomy.When analyzed in the aggregate, image-based models for anatomicalcavities, spaces and structures of the patient's anatomy, such as apatient lung network, may be generated. Techniques such as center-linegeometry may be determined and approximated from the CT images todevelop a 3D volume of the patient's anatomy, referred to aspreoperative model data 91. The use of center-line geometry is discussedin U.S. patent application Ser. No. 14/523,760, the contents of whichare herein incorporated in its entirety. Network topological models mayalso be derived from the CT-images, and are particularly appropriate forbronchoscopy.

In some embodiments, the instrument may be equipped with a camera toprovide vision data 92. The localization module 95 may process thevision data to enable one or more vision-based location tracking. Forexample, the preoperative model data may be used in conjunction with thevision data 92 to enable computer vision-based tracking of the medicalinstrument (e.g., an endoscope or an instrument advance through aworking channel of the endoscope). For example, using the preoperativemodel data 91, the robotic system may generate a library of expectedendoscopic images from the model based on the expected path of travel ofthe endoscope, each image linked to a location within the model.Intra-operatively, this library may be referenced by the robotic systemin order to compare real-time images captured at the camera (e.g., acamera at a distal end of the endoscope) to those in the image libraryto assist localization.

Other computer vision-based tracking techniques use feature tracking todetermine motion of the camera, and thus the endoscope. Some feature ofthe localization module 95 may identify circular geometries in thepreoperative model data 91 that correspond to anatomical lumens andtrack the change of those geometries to determine which anatomical lumenwas selected, as well as the relative rotational and/or translationalmotion of the camera. Use of a topological map may further enhancevision-based algorithms or techniques.

Optical flow, another computer vision-based technique, may analyze thedisplacement and translation of image pixels in a video sequence in thevision data 92 to infer camera movement. Examples of optical flowtechniques may include motion detection, object segmentationcalculations, luminance, motion compensated encoding, stereo disparitymeasurement, etc. Through the comparison of multiple frames overmultiple iterations, movement and location of the camera (and thus theendoscope) may be determined.

The localization module 95 may use real-time EM tracking to generate areal-time location of the endoscope in a global coordinate system thatmay be registered to the patient's anatomy, represented by thepreoperative model. In EM tracking, an EM sensor (or tracker) comprisingof one or more sensor coils embedded in one or more locations andorientations in a medical instrument (e.g., an endoscopic tool) measuresthe variation in the EM field created by one or more static EM fieldgenerators positioned at a known location. The location informationdetected by the EM sensors is stored as EM data 93. The EM fieldgenerator (or transmitter), may be placed close to the patient to createa low intensity magnetic field that the embedded sensor may detect. Themagnetic field induces small currents in the sensor coils of the EMsensor, which may be analyzed to determine the distance and anglebetween the EM sensor and the EM field generator. These distances andorientations may be intra-operatively “registered” to the patientanatomy (e.g., the preoperative model) in order to determine thegeometric transformation that aligns a single location in the coordinatesystem with a position in the pre-operative model of the patient'sanatomy. Once registered, an embedded EM tracker in one or morepositions of the medical instrument (e.g., the distal tip of anendoscope) may provide real-time indications of the progression of themedical instrument through the patient's anatomy.

Robotic command and kinematics data 94 may also be used by thelocalization module 95 to provide localization data 96 for the roboticsystem. Device pitch and yaw resulting from articulation commands may bedetermined during pre-operative calibration. Intra-operatively, thesecalibration measurements may be used in combination with known insertiondepth information to estimate the position of the instrument.Alternatively, these calculations may be analyzed in combination withEM, vision, and/or topological modeling to estimate the position of themedical instrument within the network.

As FIG. 15 shows, a number of other input data can be used by thelocalization module 95. For example, although not shown in FIG. 15 , aninstrument utilizing a shape-sensing fiber can provide shape data thatthe localization module 95 can use to determine the location and shapeof the instrument.

The localization module 95 may use the input data 91-94 incombination(s). In some cases, such a combination may use aprobabilistic approach where the localization module 95 assigns aconfidence weight to the location determined from each of the input data91-94. Thus, where the EM data may not be reliable (as may be the casewhere there is EM interference) the confidence of the locationdetermined by the EM data 93 can be decrease and the localization module95 may rely more heavily on the vision data 92 and/or the roboticcommand and kinematics data 94.

As discussed above, the robotic systems discussed herein may be designedto incorporate a combination of one or more of the technologies above.The robotic system's computer-based control system, based in the tower,bed and/or cart, may store computer program instructions, for example,within a non-transitory computer-readable storage medium such as apersistent magnetic storage drive, solid state drive, or the like, that,upon execution, cause the system to receive and analyze sensor data anduser commands, generate control signals throughout the system, anddisplay the navigational and localization data, such as the position ofthe instrument within the global coordinate system, anatomical map, etc.

2. Introduction to Location Sensor-Based Branch Prediction.

Embodiments of the disclosure relate to systems and techniques forlocation sensor-based branch prediction. The system may employ locationsensor(s) or location sensing device(s) to localize the distal end of aninstrument, for example, during a medical procedure. The locationsensor(s) may be positioned at or near the distal end of the instrumentor may be positioned remote from the distal end of the instrument.Examples of location sensors or location sensing devices which may bepositioned at or near the distal end of the instrument include EMsensors, vision-based location sensors (e.g., a camera), shape sensingfibers, etc. Examples of location sensors or location sensing deviceswhich may be positioned remotely from the distal end of the instrumentinclude fluoroscopic imaging devices, robotic system component(s) thatgenerate or process robotic data for controlling the position of theinstrument via one or more instrument manipulators, remote vision-basedlocation sensors, etc.

The location sensors may be configured to generate location dataindicative of the location of the distal end of the instrument, forexample, with respect to a location sensor coordinate system. As usedherein, the location sensor coordinate system may refer to anycoordinate system which can be used to define or determine the positionsof the location data (e.g., on a manifold such as Euclidean space)generated by the location sensors. When the location sensors arecollocated with the distal end of the instrument, the location data maybe representative of the location of the location sensors themselves,which the processor can then use to determine the location of the distalend of the instrument. In certain embodiments, the location sensorcoordinate system may comprise a set of axes and an origin, which may bedefined based on the particular technology used to implement thelocation sensors.

For example, EM sensors located in or on the instrument may beconfigured to measure an EM field generated by an EM field generator.The properties of the EM field, and thus the EM values measured by theEM sensors, may be defined with respect to the location and orientationof the EM field generator. Thus, the positioning of the EM fieldgenerator may affect the values measured by the EM sensors and may alsodefine the location and orientation of the EM coordinate system.

As described above, a luminal network of a patient may bepre-operatively mapped using, for example, low dose CT scans to producea model of the luminal network. Since the model may be produced via adifferent technique than used to locate the distal end of theinstrument, the model coordinate system may not be aligned with thelocation sensor coordinate system. Accordingly, in order to use thelocation sensor coordinate system to track the location of theinstrument with respect to the model, one technique may involveregistering (e.g., by one or more components of a robotic system or aseparate system communicatively coupled to the robotic system, includingbut limited to a processor, a localization system, a localizationmodule, etc.) a coordinate system used by one or more location sensorswith another coordinate system, such as a coordinate system used by ananatomical model. This registration may include, for example,translation and/or rotation applied to the location sensor data in orderto map the location sensor data from the location sensor coordinatesystem into the model coordinate system.

The system or processor may perform registration of a location sensorcoordinate system to the model coordinate system, for example, during aninitial phase of the procedure. Depending on the implementation, theprocessor may perform the registration process automatically in thebackground as the instrument is initially advanced through the luminalnetwork. In another implementation, the processor may provide a set ofinstructions to the user to drive the instrument to specific locationswithin the luminal network or along a set registration path tofacilitation the registration process. Accordingly, the processor mayperform a portion of the procedure while the location data received fromthe location sensor(s) is not registered to the model.

In order to provide feedback to the user regarding the navigation of theinstrument during the medical procedure, a “fusion” localizationalgorithm may be run (e.g., by the localization system 90 of FIG. 15 ).The fusion algorithm may combine data indicative of the location of thedistal end of the instrument received from a plurality of sources todetermine the location of the instrument. One function of the fusionalgorithm may also include the prediction of the next branch of theluminal network into which the instrument may be advanced. Thisprediction may be based on at least some of the data sources used in thefusion algorithm, including the location sensor data. In certainembodiments, the prediction may also be used as an input in thedetermination of the location of the distal end of the instrument.

Since the location sensor(s) may not be registered for at least aportion of the medical procedure, certain aspects of this disclosure mayrelate to techniques for branch prediction which may be employed basedon registered location sensor data or unregistered location sensor data(also generally referred to as “raw” location sensor data). Thus, thesystem may selectively apply different techniques or combinationsthereof for location sensor-based branch prediction depending on whetherthe location sensor(s) have been registered.

A. EM Navigation-Guided Bronchoscopy.

Hereinafter, an example system which may employ the techniques forlocation sensor-based branch prediction will be described. For example,the system may be configured for an EM navigation-guided bronchoscopicprocedure. However, aspects of this disclosure may also apply to systemswhich use other location sensors which can produce location data as wellas to other types of medical procedures.

FIG. 16A illustrates an example operating environment 100 which canimplement one or more aspects of the disclosed branch prediction systemsand techniques. The operating environment 100 can include a platform 102supporting a patient 101, a surgical or medical robotic system 110guiding movement of an instrument 115, a command center 105 forcontrolling operations of the robotic system 110, EM controller 135, EMfield generator 120, and EM sensors 125, 130. FIG. 16A also illustratesan outline of a region of a luminal network 140 within the patient 101,shown in more detail in FIG. 16B.

The system 110 can include one or more robotic arms for positioning andguiding movement of the instrument 115 through the luminal network 140of the patient 101. The command center 105 can be communicativelycoupled to the robotic system 110 for receiving position data and/orproviding control signals generated based on user commands received froma user. As used herein, “communicatively coupled” refers to any wiredand/or wireless data transfer mediums, including but not limited to awireless wide area network (WWAN) (e.g., one or more cellular networks),a wireless local area network (WLAN) (e.g., configured for one or morestandards, such as the IEEE 802.11 (Wi-Fi)), Bluetooth, data transfercables, and/or the like. The robotic system 110 can be any of thesystems described above with respect to FIGS. 1-15 .

The instrument 115 may be a tubular and flexible surgical instrumentthat is inserted into the anatomy of a patient to capture images of theanatomy (e.g., body tissue) and provide a working channel for insertionof other medical instruments to a target tissue site. As describedabove, the instrument 115 can be a procedure-specific endoscope, forexample a bronchoscope, gastroscope, or ureteroscope, or may be alaparoscope or vascular steerable catheter. The instrument 115 caninclude one or more imaging devices (e.g., cameras or other types ofoptical sensors) at its distal end. The imaging devices may include oneor more optical components such as an optical fiber, fiber array,photosensitive substrate, and/or lens(es). The optical components movealong with the tip of the instrument 115 such that movement of the tipof the instrument 115 results in corresponding changes to the field ofview of the images captured by the imaging devices. The distal end ofthe instrument 115 can be provided with one or more EM sensors 125 fortracking the position of the distal end within an EM field generatedaround the luminal network 140.

The EM controller 135 can control the EM field generator 120 to producea varying EM field. The EM field can be time-varying and/or spatiallyvarying, depending upon the embodiment. The EM field generator 120 canbe an EM field generating board in some embodiments. Some embodiments ofthe disclosed systems can use an EM field generator board positionedbetween the patient and the platform 102 supporting the patient 101, andthe EM field generator board can incorporate a thin barrier thatminimizes tracking distortions caused by conductive or magneticmaterials located below it. In other embodiments, an EM field generatorboard can be mounted on a robotic arm, for example, similar to thoseshown in the robotic system 110, which can offer flexible setup optionsaround the patient.

FIG. 16B illustrates an example luminal network 140 that can benavigated in the operating environment 100 of FIG. 16A. The luminalnetwork 140 includes the branched structure of the airways 150 of thepatient 101, the trachea 154 leading to the main carina 156 (typicallythe first bifurcation encountered during bronchoscopy navigation), and anodule (or lesion) 155 that can be accessed as described herein fordiagnosis and/or treatment. As illustrated, the nodule 155 is located atthe periphery of the airways 150. The instrument 115 may comprise asheath 141 having a first diameter and thus the distal end of the sheath141 may not able to be positioned through the smaller-diameter airwaysaround the nodule 155. Accordingly, a scope 145 extends from the workingchannel of the instrument 115 and across the remaining distance to thenodule 155. The scope 145 may have a lumen through which instruments,for example, biopsy needles, cytology brushes, and/or tissue samplingforceps, can be passed to the target tissue site of nodule 155. In suchimplementations, both the distal end of the sheath 141 and the distalend of the scope 145 can be provided with EM sensors for tracking theirrespective positions within the airways 150.

In some embodiments, a two-dimensional (2D) display of a 3D luminalnetwork model as described herein, or a cross-section of the 3D model,can resemble FIG. 16B. An estimated location of the distal end of theinstrument can be overlaid onto such a representation. In certainimplementations, the estimated location may be displayed on a display ofa command console, such as the command console 160 illustrated in FIG.16C.

FIG. 16C illustrates an example command console 160 that can be used,for example, as the command console 105 in the example operatingenvironment 100. The command console 160 may include a console base 161,one or more display 162 (e.g., monitors), and one or more controlmodules (e.g., a keyboard 163 and joystick 164). In some embodiments,one or more of the command console 160 functionality may be integratedinto a base 180 of the robotic system 110 or another systemcommunicatively coupled to the robotic system 110. A user 165, e.g., aphysician, remotely controls the robotic system 110 from an ergonomicposition using the command console 160.

The console base 161 may include a central processing unit, a memoryunit, a data bus, and associated data communication ports that areresponsible for interpreting and processing signals such as cameraimagery and tracking sensor data, e.g., from the instrument 115 shown inFIGS. 16A and 16B. In some embodiments, both the console base 161 andthe base 180 perform signal processing for load-balancing. The consolebase 161 may also process commands and instructions provided by the user165 through the control modules 163 and 164. In addition to the keyboard163 and joystick 164 shown in FIG. 16C, the control modules may includeother devices, for example, computer mice, trackpads, trackballs,control pads, controllers such as handheld remote controllers, andsensors (e.g., motion sensors or cameras) that capture hand gestures andfinger gestures. A controller can include a set of user inputs (e.g.,buttons, joysticks, directional pads, etc.) mapped or linked to anoperation of the instrument (e.g., articulation, driving, waterirrigation, etc.).

The displays 162 may include electronic monitors (e.g., LCD displays,LED displays, touch-sensitive displays), virtual reality viewingdevices, e.g., goggles or glasses, and/or other display devices. In someembodiments, the display modules 162 are integrated with the controlmodules, for example, as a tablet device with a touchscreen. In someembodiments, one of the displays 162 can display a 3D model of thepatient's luminal network and virtual navigation information (e.g., avirtual representation of the end of the endoscope within the modelbased on EM sensor position) while the other of the displays 162 candisplay image information received from the camera or another sensingdevice at the end of the instrument 115. In some implementations, theuser 165 can both view data and input commands to the system 110 usingthe integrated displays 162 and control modules. The displays 162 candisplay 2D renderings of 3D images and/or 3D images using a stereoscopicdevice, e.g., a visor or goggles. The 3D images provide an “endo view”(i.e., endoscopic view), which is a computer 3D model illustrating theanatomy of a patient. The “endo view” provides a virtual environment ofthe patient's interior and an expected location of an instrument 115inside the patient. A user 165 compares the “endo view” model to actualimages captured by a camera to help mentally orient and confirm that theinstrument 115 is in the correct—or approximately correct—locationwithin the patient. The “endo view” provides information aboutanatomical structures, e.g., the shape of airways, circulatory vessels,or an intestine or colon of the patient, around the distal end of theinstrument 115. The display modules 162 can simultaneously display the3D model and CT scans of the anatomy the around distal end of theinstrument 115. Further, the display modules 162 may overlay the alreadydetermined navigation paths of the instrument 115 on the 3D model and CTscans.

In some embodiments, a model of the instrument 115 is displayed with the3D models to help indicate a status of a surgical procedure. Forexample, the CT scans identify a lesion in the anatomy where a biopsymay be necessary. During operation, the display modules 162 may show areference image captured by the instrument 115 corresponding to thecurrent location of the instrument 115. The display modules 162 mayautomatically display different views of the model of the instrument 115depending on user settings and a particular surgical procedure. Forexample, the display modules 162 show an overhead fluoroscopic view ofthe instrument 115 during a navigation step as the instrument 115approaches an operative region of a patient.

B. Location Sensor-Based Branch Prediction Using Unregistered LocationData.

As discussed above, an initial phase of a medical procedure may beperformed before the location sensor(s) are registered to a model of theluminal network. However, the location sensor(s) may still producelocation data prior to registration. Although unregistered to the model,the raw location sensor data may be useful to provide certainlocalization and navigational functionality. For example, the processorcan determine the relative orientations of the instrument at differenttimes based on the raw, unregistered location data. Thus, in certainembodiments, based on the shape and structure of the luminal network andthe orientation of the instrument determined based on the unregistereddata, the processor may facilitate predicting the next branch of theluminal network into which the instrument is likely to be advanced.

Branch prediction may be included as part of a navigation configurationsystem. FIGS. 17A-17D show example block diagrams of a navigationconfiguration system 200, according to one embodiment. Morespecifically, FIG. 17A provides an overview of an example block diagramof the navigation configuration system 200, according to one embodiment.In FIG. 17A, the navigation configuration system 200 includes multipleinput data stores, a navigation module 205 that receives various typesof input data from the multiple input data stores, and an outputnavigation data store 290 that receives output navigation data from thenavigation module. The block diagram of the navigation configurationsystem 200 shown in FIG. 17A is merely one example, and in alternativeembodiments not shown, the navigation configuration system 200 caninclude different and/or addition entities. Likewise, functionsperformed by various entities of the system 200 may differ according todifferent embodiments. The navigation configuration system 200 may besimilar to the navigational system described in U.S. Patent PublicationNo. 2017/0084027, published on Mar. 23, 2017, the entirety of which isincorporated herein by reference.

The input data, as used herein, refers to raw data gathered from and/orprocessed by input devices (e.g., command module(s), optical sensor(s),EM sensor(s), IDM(s)) for generating estimated state information for theendoscope as well as output navigation data. The multiple input datastores 210-240 include an image data store 210, an EM data store 220, arobot data store 230, and a 3D model data store 240. Each type of theinput data stores 210-240 stores the name-indicated type of data foraccess and use by a navigation module 205. Image data may include one ormore image frames captured by the imaging device at the instrument tip,as well as information such as frame rates or timestamps that allow adetermination of the time elapsed between pairs of frames. Robot datamay include data related to physical movement of the medical instrumentor part of the medical instrument (e.g., the instrument tip or sheath)within the tubular network. Example robot data includes command datainstructing the instrument tip to reach a specific anatomical siteand/or change its orientation (e.g., with a specific pitch, roll, yaw,insertion, and retraction for one or both of a leader and a sheath)within the tubular network, insertion data representing insertionmovement of the part of the medical instrument (e.g., the instrument tipor sheath), IDM data, and mechanical data representing mechanicalmovement of an elongate member of the medical instrument, for examplemotion of one or more pull wires, tendons or shafts of the endoscopethat drive the actual movement of the medial instrument within thetubular network. EM data may be collected by EM sensors and/or the EMtracking system as described above. 3D model data may be derived from 2DCT scans as described above. Path data includes the planned navigationpath which may be generated by a topological search of the tubularnetwork to one or more targets.

The output navigation data store 290 receives and stores outputnavigation data provided by the navigation module 205. Output navigationdata indicates information to assist in directing the medical instrumentthrough the tubular network to arrive at a particular destination withinthe tubular network, and is based on estimated state information for themedical instrument at each instant time, the estimated state informationincluding the location and orientation of the medical instrument withinthe tubular network. In one embodiment, as the medical instrument movesinside the tubular network, the output navigation data indicatingupdates of movement and location/orientation information of the medicalinstrument is provided in real time, which better assists its navigationthrough the tubular network.

To determine the output navigation data, the navigation module 205locates (or determines) the estimated state of the medical instrumentwithin a tubular network. As shown in FIG. 17A, the navigation module205 further includes various algorithm modules, such as an EM-basedalgorithm module 250, an image-based algorithm module 260, and arobot-based algorithm module 270, that each may consume mainly certaintypes of input data and contribute a different type of data to a stateestimator 280. As illustrated in FIG. 17A, the different kinds of dataoutput by these modules, labeled EM-based data, the image-based data,the robot-based data, and the path-based data, may be generally referredto as “intermediate data” for sake of explanation.

FIG. 17B shows an example block diagram of the navigation module 205shown in FIG. 17A, according to one embodiment. As introduced above, thenavigation module 205 further includes a state estimator 280 as well asmultiple algorithm modules that employ different algorithms fornavigating through a tubular network. For clarity of description, thestate estimator 280 is described first, followed by the description ofthe various modules that exchange data with the state estimator 280.

The state estimator 280 included in the navigation module 205 receivesvarious intermediate data and provides the estimated state of theinstrument tip as a function of time, where the estimated stateindicates the estimated location and orientation information of theinstrument tip within the tubular network. The estimated state data arestored in the estimated data store 285 that is included in the stateestimator 280.

FIG. 17C shows an example block diagram of the estimated state datastore 285 included in the state estimator 280, according to oneembodiment. The estimated state data store 285 may include a bifurcationdata store 286, a position data store 287, a depth data store 288, andan orientation data store 289, however this particular breakdown of datastorage is merely one example, and in alternative embodiments not shown,different and/or additional data stores can be included in the estimatedstate data store 285.

The various stores introduced above represent estimated state data in avariety of ways. Specifically, bifurcation data refers to the locationof the medical instrument with respect to the set of branches (e.g.,bifurcation, trifurcation or a division into more than three branches)within the tubular network. For example, the bifurcation data can be setof branch choices elected by the instrument as it traverses through thetubular network, based on a larger set of available branches asprovided, for example, by the 3D model which maps the entirety of thetubular network. The bifurcation data can further include information infront of the location of the instrument tip, such as branches(bifurcations) that the instrument tip is near but has not yet traversedthrough, but which may have been detected, for example, based on thetip's current position information relative to the 3D model, or based onimages captured of the upcoming bifurcations.

Position data indicates three-dimensional position of some part of themedical instrument within the tubular network or some part of thetubular network itself. Position data can be in the form of absolutelocations or relative locations relative to, for example, the 3D modelof the tubular network. As one example, position data can include anindication of the position of the location of the instrument beingwithin a specific branch. The identification of the specific branch mayalso be stored as a segment identification (ID) which uniquelyidentifies the specific segment of the model in which the instrument tipis located.

Depth data indicates depth information of the instrument tip within thetubular network. Example depth data includes the total insertion(absolute) depth of the medical instrument into the patient as well asthe (relative) depth within an identified branch (e.g., the segmentidentified by the position data store 287). Depth data may be determinedbased on position data regarding both the tubular network and medicalinstrument.

Orientation data indicates orientation information of the instrumenttip, and may include overall roll, pitch, and yaw in relation to the 3Dmodel as well as pitch, roll, raw within an identified branch.

Turning back to FIG. 17B, the state estimator 280 provides the estimatedstate data back to the algorithm modules for generating more accurateintermediate data, which the state estimator uses to generate improvedand/or updated estimated states, and so on forming a feedback loop. Forexample, as shown in FIG. 17B, the EM-based algorithm module 250receives prior EM-based estimated state data, also referred to as dataassociated with timestamp “t-1.” The state estimator 280 uses this datato generate “estimated state data (prior)” that is associated withtimestamp “t-1.” The state estimator 280 then provides the data back tothe EM-based algorithm module. The “estimated state data (prior)” may bebased on a combination of different types of intermediate data (e.g.,robotic data, image data) that is associated with timestamp “t-1” asgenerated and received from different algorithm modules. Next, theEM-based algorithm module 250 runs its algorithms using the estimatedstate data (prior) to output to the state estimator 280 improved andupdated EM-based estimated state data, which is represented by “EM-basedestimated state data (current)” here and associated with timestamp t.This process continues to repeat for future timestamps as well.

As the state estimator 280 may use several different kinds ofintermediate data to arrive at its estimates of the state of the medicalinstrument within the tubular network, the state estimator 280 isconfigured to account for the various different kinds of errors anduncertainty in both measurement and analysis that each type ofunderlying data (robotic, EM, image, path) and each type of algorithmmodule might create or carry through into the intermediate data used forconsideration in determining the estimated state. To address these, twoconcepts are discussed, that of a probability distribution and that ofconfidence value.

The “probability” of the “probability distribution”, as used herein,refers to a likelihood of an estimation of a possible location and/ororientation of the medical instrument being correct. For example,different probabilities may be calculated by one of the algorithmmodules indicating the relative likelihood that the medical instrumentis in one of several different possible branches within the tubularnetwork. In one embodiment, the type of probability distribution (e.g.,discrete distribution or continuous distribution) is chosen to matchfeatures of an estimated state (e.g., type of the estimated state, forexample continuous position information vs. discrete branch choice). Asone example, estimated states for identifying which segment the medicalinstrument is in for a trifurcation may be represented by a discreteprobability distribution, and may include three discrete values of 20%,30% and 50% representing chance as being in the location inside each ofthe three branches as determined by one of the algorithm modules. Asanother example, the estimated state may include a roll angle of themedical instrument of 40±5 degrees and a segment depth of the instrumenttip within a branch may be is 4±1 mm, each represented by a Gaussiandistribution which is a type of continuous probability distribution.Different methods or modalities can be used to generate theprobabilities, which will vary by algorithm module as more fullydescribed below with reference to later figures.

In contrast, the “confidence value,” as used herein, reflects a measureof confidence in the estimation of the state provided by one of thealgorithms based one or more factors. For the EM-based algorithms,factors such as distortion to EM Field, inaccuracy in EM registration,shift or movement of the patient, and respiration of the patient mayaffect the confidence in estimation of the state. Particularly, theconfidence value in estimation of the state provided by the EM-basedalgorithms may depend on the particular respiration cycle of thepatient, movement of the patient or the EM field generators, and thelocation within the anatomy where the instrument tip locates. For theimage-based algorithms, examples factors that may affect the confidencevalue in estimation of the state include illumination condition for thelocation within the anatomy where the images are captured, presence offluid, tissue, or other obstructions against or in front of the opticalsensor capturing the images, respiration of the patient, condition ofthe tubular network of the patient itself (e.g., lung) such as thegeneral fluid inside the tubular network and occlusion of the tubularnetwork, and specific operating techniques used in, e.g., navigating orimage capturing.

For example one factor may be that a particular algorithm has differinglevels of accuracy at different depths in a patient's lungs, such thatrelatively close to the airway opening, a particular algorithm may havea high confidence in its estimations of medical instrument location andorientation, but the further into the bottom of the lung the medicalinstrument travels that confidence value may drop. Generally, theconfidence value is based on one or more systemic factors relating tothe process by which a result is determined, whereas probability is arelative measure that arises when trying to determine the correct resultfrom multiple possibilities with a single algorithm based on underlyingdata.

As one example, a mathematical equation for calculating results of anestimated state represented by a discrete probability distribution(e.g., branch/segment identification for a trifurcation with threevalues of an estimated state involved) can be as follows:

S ₁ =C _(EM) *P _(1,EM) +C _(Image) *P _(1,Image) +C _(Robot) *P_(1,Robot)

S ₂ =C _(EM) *P _(2,EM) +C _(Image) *P _(2,Image) +C _(Robot) *P_(2,Robot)

S ₃ =C _(EM) *P _(3,EM) +C _(Image) *P _(3,Image) +C _(Robot) *P_(3,Robot)

In the example mathematical equation above, S_(i)(i=1, 2, 3) representspossible example values of an estimated state in a case where 3 possiblesegments are identified or present in the 3D model, C_(EM), C_(Image),and C_(Robot) represents confidence value corresponding to EM-basedalgorithm, image-based algorithm, and robot-based algorithm andP_(i,Em), P_(i,Image), and P_(i,Robot) represent the probabilities forsegment i.

To better illustrate the concepts of probability distributions andconfidence value associated with estimate states, a detailed example isprovided here. In this example, a user is trying to identify segmentwhere an instrument tip is located in a certain trifurcation within acentral airway (the predicted region) of the tubular network, and threealgorithms modules are used including EM-based algorithm, image-basedalgorithm, and robot-based algorithm. In this example, a probabilitydistribution corresponding to the EM-based algorithm may be 20% in thefirst branch, 30% in the second branch, and 50% in the third (last)branch, and the confidence value applied to this EM-based algorithm andthe central airway is 80%. For the same example, a probabilitydistribution corresponding to the image-based algorithm may be 40%, 20%,40% for the first, second, and third branch, and the confidence valueapplied to this image-based algorithm is 30%; while a probabilitydistribution corresponding to the robot-based algorithm may be 10%, 60%,30% for the first, second, and third branch, and the confidence valueapplied to this image-based algorithm is 20%. The difference ofconfidence values applied to the EM-based algorithm and the image-basedalgorithm indicates that the EM-based algorithm may be a better choicefor segment identification in the central airway, compared with theimage-based algorithm. An example mathematical calculation of a finalestimated state can be:

for the first branch: 20%*80%+40%*30%+10%*20%=30%; for the secondbranch: 30%*80%+20%*30%+60%*20%=42%; and for the third branch:50%*80%+40%*30%+30%*20%=58%.

In this example, the output estimated state for the instrument tip canbe the result values (e.g., the resulting 30%, 42% and 58%), orderivative value from these result values such as the determination thatthe instrument tip is in the third branch. Although this exampledescribes the use of the algorithm modules include EM-based algorithm,image-based algorithm, and robot-based algorithm, the estimation of thestate for the instrument tip can also be provided based on differentcombinations of the various algorithms modules, including the path-basedalgorithm.

As above the estimated state may be represented in a number of differentways. For example, the estimated state may further include an absolutedepth from airway to location of the tip of the instrument, as well as aset of data representing the set of branches traversed by the instrumentwithin the tubular network, the set being a subset of the entire set ofbranches provided by the 3D model of the patient's lungs, for example.The application of probability distribution and confidence value onestimated states allows improved accuracy of estimation of locationand/or orientation of the instrument tip within the tubular network.

As shown in FIG. 17B, the algorithm modules include an EM-basedalgorithm module 250, an image-based algorithm module 260, and arobot-based algorithm module 270. The algorithm modules shown in FIG.17B is merely one example, and in alternative embodiments, differentand/additional algorithm modules involving different and/or additionalnavigation algorithms can also be included in the navigation module 205.

B.1. Branch Prediction System

The EM-based algorithm module 250 further includes an EM registrationmodule 252, an EM localization module 254, and an EM branch predictionmodule 256. The EM registration module 252 may perform registration ofEM coordinates to 3D model coordinates. The EM localization module 254may determine an estimate of the position and orientation of theinstrument. The EM branch prediction module 256 may determine aprediction as to which segment of the model the instrument will advancefrom a current segment in which the instrument is currently located.

FIG. 17D illustrates an example location sensor-based localization andbranch prediction system in accordance with aspects of this disclosure.In particular, FIG. 17D shows a location sensor-based localization andbranch prediction system that includes EM data store 220, registrationtransform data store 253, 3D model data store 240, EM localizationmodule 254, EM branch predication module 256, and estimated state datastore 285. The block diagram of the localization and branch predictionsystem is merely one example, and in other embodiments not shown, thelocalization and branch prediction system can include different and/oradditional components, for example, in certain implementations thebranch prediction system 210 may include one or more of image data store220 and robot data store 230 in place of or in addition to EM data store220.

The EM localization module 254 receives as inputs, estimated state data(prior) (e.g., bifurcation data) from the estimated state data store285, the EM data from the EM data store 220, registration transform datafrom the registration transform data store 253, as well as 3D model datafrom the 3D model data store 240. Based on the received data, the EMlocalization module 254 determines an estimate of the position andorientation of the instrument tip relative to the 3D model of thetubular network and provides EM-based estimated state data (current) tothe state estimator 280. As an example, the EM-based estimated statedata may be represented as a probability distribution (e.g., a discretedistribution of 20%, 30% and 50% for three segments of a trifurcation,as described above). Additionally, when at a bifurcation as indicated bythe received bifurcation data, the EM localization module 254 maycompare the pitch and yaw of the tip of the instrument to the angles ofeach branch in the 3D model to estimate which branch has been selectedby the user for traversal. The EM localization module 254 outputs theEM-based estimated state data (current) to the estimated data store 285and the EM branch prediction module 256.

The EM branch prediction module 256 may determine a prediction as towhich segment of the model the instrument will advance. For example,based on the determined current segment of the model in which theinstrument is located and/or orientation data received from theorientation data store 289, the EM branch prediction module 256 maydetermine a prediction that the instrument will advance into each of thechild segments of the current segment. There may be a number ofdifferent techniques which may be employed by the EM branch predictionmodule 256 for determining the prediction, which will be described ingreater detail below. In some embodiments, the specific technique usedby the EM branch prediction module 256 may depend on whether thelocation data has been registered to the model. The EM branch predictionmodule 256 may provide the determined prediction to the estimated statedata store 285. In some embodiments as discussed above, the EM branchprediction module 256 may determine the predication based on orientationdata received from the orientation data store 289. In other embodiments,the EM branch prediction module 256 may determine the predication basedon position data from the position data store 287 or based on both theorientation data and the position data.

B.2. Example Route Taken by Instrument

For illustrative purposes, aspects of this disclosure related tolocation sensor-based branch prediction will be explained in the contextof bronchoscopy and navigating portions of the bronchial luminalnetwork. However, the present disclosure can also be applied to otherluminal networks and other medical procedures.

FIG. 18A illustrates an example luminal network in which locationsensor-based branch prediction can be performed in accordance withaspects of this disclosure. In the embodiment of FIG. 18A, theillustrated luminal network 300 corresponds to airways of a patient'slungs and includes a first generation airway 305 (e.g., a trachea) whichbranches into two second generation airways 315 and 320 (e.g., primarybronchi) at a main carina 310. Assuming the patient is lying supine, thetrachea 305 will branch into the patient's left bronchi 320 and thepatient's right bronchi 315.

FIG. 18A also illustrates a route 325 along which the instrument may bedriven as the instrument is navigated through the airways during amedical procedure. Two example tracked locations 330 and 335 of theinstrument when driven along the route 325 are shown and will bereferenced in discussing various embodiments. The location sensors maygenerate location data (not illustrated) representative of the trackedlocations 330 and 335 within the location sensor coordinate system, asdescribed in detail in connection with FIG. 18B below. Aspects of thisdisclosure may relate to the prediction of which airway the instrumentwill advance into between one of the second generation airways 315 and320. As will be described in detail later, the processor may select aninitial location 330 and a subsequent location 335 of the instrument foruse in the branch prediction technique.

B.3. Example Location Data Generated During Procedure

FIG. 18B illustrates an example set of location data points which may begenerated by one or more location sensors as an instrument is driventhrough a luminal network in accordance with aspects of this disclosure.In FIG. 18B, a navigation path 345 may be defined as a path along whichthe instrument is to be driven during the procedure. The navigation path345 may be selected by a user prior to performing the procedure,generated by a path planning program, or the like. For the sake ofconvenience, FIG. 18B is illustrated in 2D, however, it is to beappreciated that this disclosure can be also applied to a 3Dnavigational path. The navigation path 345 may be defined with respectto a model coordinate system 350 in which a model (e.g., theskeleton-based model 500 of FIG. 20 ) of the patient's luminal networkis defined.

During a procedure, the location sensors may generate a plurality ofunregistered data points 355 which represent the tracked locations ofthe instrument as the instrument is navigated through the airways. Theunregistered data points 355 may be defined with respect to a locationsensor coordinate system 360. In the case of embodiments utilizing an EMsensors, the location sensor coordinate system 360 may be defined by orcorrespond to the EM field from the EM generator A navigation system(e.g., the localization system shown in FIG. 15 ) operating with arobotic system may determine a registration between the location sensorcoordinate system 360 and the model coordinate system 350 which can beused to map the unregistered data points 355 into the model coordinatesystem 350. The registration may be stored in memory, for example, inthe registration data store 225 of FIG. 17 . One technique forregistering the location sensor coordinate system 360 and the modelcoordinate system 350 may include selecting a transformation matrixwhich, when applied to the unregistered data points 355, minimizes thesum of the distances between the data points and the path 345. Once theregistration is applied to the unregistered data points 355, the“registered” location data points may substantially align with the path345.

B.4. Example Branch Prediction Technique

FIG. 19 is a flowchart illustrating an example method operable by arobotic system, or component(s) thereof, for location sensor-basedbranch prediction in accordance with aspects of this disclosure. Forexample, the steps of method 400 illustrated in FIG. 19 may be performedby processor(s) and/or other component(s) of a robotic system orassociated system(s). For convenience, the method 400 is described asperformed by the location sensor-based branch prediction system, also isalso referred to simply as the “system” in connection with thedescription of the method 400.

At block 405, the location sensor-based branch prediction systemdetermines a first orientation of an instrument based on first locationdata generated by a set of one or more location sensors for theinstrument. The first location data may be indicative of the location ofthe instrument in a location sensor coordinate system at a first time.In certain embodiments, the location sensors may be located at or near adistal end of the instrument, and thus, the location data produced bythe location sensors may be indicative of the location of the distal endof the instrument. In one embodiment, the first orientation of theinstrument may correspond to the orientation of the instrument at aninitial location, such as the initial location 330 of FIG. 18 .

At block 410, the location sensor-based branch prediction systemdetermines a second orientation of the instrument at a second time basedon second location data generated by the set of location sensors. Thedistal end of the instrument may be located within a first segment ofthe model at the first time and the second time. The first segment maybranch into two or more child segments. In the example embodimentillustrated in FIG. 18 , the distal end of the instrument at the firsttime and the second time may respectively correspond to the initiallocation 330 and the subsequent location 335, each of which are locatedwithin the first generation airway 305 which branches into two secondgeneration airways 315 and 320.

At block 415, the location sensor-based branch prediction systemdetermines data indicative of a difference between the first orientationand the second orientation. This may include for example, determiningthe orientation of the instrument, based on unregistered location sensordata, at two successive points in time. Based on the difference betweenthe orientations of the instrument while positioned in the currentsegment, the system may be able to predict into which of the two or morechild segments the instrument is most likely to be advanced. That is,since the system has access to the orientation of each of the firstgeneration airway 305 which branches into two second generation airways315 and 320 from the model, the system may be able to predict which ofthe child segments the instrument is most likely to be advanced based ona change in the orientation of the instrument.

In one implementation, the location sensor-based branch predictionsystem may determine the difference between the initial orientation anda subsequent orientation by calculating the relative transformationmatrix between the initial orientation and the subsequent orientation.The system may decompose the transformation matrix into the roll, pitch,and yaw angles, defining the orientation of the instrument in thelocation sensor coordinate system. Certain location sensor technologies(e.g., EM location sensors) which can be employed as the locationsensor(s) may be substantially aligned with the patient in at least oneangular degree of freedom when the patient lies supine for a givenprocedure (e.g., for a bronchoscopy procedure). In the EMimplementation, an EM field generator may generate an EM field having anorientation that is defined with respect to the orientation of the EMfield generator. Thus, by arranging the orientation of the EM fieldgenerator to be aligned with the patient, the system may be able toperform the branch prediction method 400 using only the yaw angledetermined from the relative transformation matrix.

Since the orientation of the patient and the EM field may be known for abronchoscopy procedure, the system may be able to determine a yaw anglebetween the initial orientation and the subsequent orientation based onthe data generated by the EM sensor. The bifurcation of the trachea intothe primary bronchi can thus be defined with respect to the yaw axis inthe EM sensor coordinate system. Accordingly, when the instrument islocated within the trachea 305 (see FIG. 18A), the yaw angle of theinstrument determined based on the EM field may substantially align withthe difference in orientation between the primary bronchi 315 and 320.Thus, the system may use a change in the yaw angle between the initialorientation and the subsequent orientation as the basis for updating thepredication of the primary bronchi 315 and 320 into which the instrumentis likely to be advanced.

In some embodiments, block 415 may also include the system determiningthe angle formed between the orientations of the child segments. Theamount of change in the orientation of the distal end of the instrumentmay correspond to the angle formed between the child segments in orderto redirect the insertion direction of the instrument from one of thechild segments to the other. As described below, the system may use theangle formed between the child segments as a factor in determiningwhether to update the branch prediction and/or as a factor used inassigning probabilities to the child segments during branch prediction.It is to be appreciated that embodiments that select thresholds based onthe angles formed between the child segments may be determined at designtime (e.g., the threshold is a parameter selected by a designer of thesystem based on the angle) or during run-time (e.g., the system includeslogic and data to dynamically determine the threshold eitherpre-operatively or intra-operatively). It is also to be appreciated thatrun-time approaches may be impacted on the hardware and software of thesystem and the anatomy of the patient to accurately distinguish betweenthe angles using the threshold.

At block 420, the location sensor-based branch prediction systemdetermines a prediction that the instrument will advance into a firstone of the child segments based on the data indicative of thedifference. Depending on the embodiment, the prediction may include: anidentification of the child segment the instrument is most likely to beadvanced, a probability to each of the child segments that theinstrument will be advanced into the corresponding child segment, anordered list of the child segments from the highest probability to thelowest probability, etc. When the system has previously determined apredication that the instrument will advance into the child segments,the system may update the prediction based on the difference inorientation determined in block 415.

In embodiments where the system determines the angle between the childsegments, the system may use the angle formed between the orientationsof the child segments in determining the prediction. In otherembodiments, the system may determine the prediction based on thedifference between the orientation of the distal end of the instrumentand the orientation of each of the child segments. In one embodiment,the system may assign a higher probability to a child segment that has asmaller difference in orientation from the orientation of the instrumentthan the remaining child segments.

In certain embodiments, the system may refrain from adjusting theprobabilities of the instrument advancing into each of the childsegments unless the orientation of the instrument has changed by morethan a threshold level. The system may, in certain embodiments, adjustthe threshold level based on the angle between the child segments. Forexample, the system may increase the threshold level when the anglebetween the child segments is greater than a first threshold angle anddecrease the threshold level when the angle between the child segmentsis less than a second threshold angle. Once the system has determined aprediction that the instrument will advance into a given one of thechild segments (e.g., based on the probability of advancing into thegiven one being greater than the probabilities for the other childsegments), the system may subsequently determine the orientation of theinstrument at a third time based on subsequent location data generatedby the set of location sensors. The system may be configured to:calculate an angle between (i) the orientation at the third time (e.g.,a time after the second time where the instrument is located at thesubsequent location) and (ii) the initial orientation; and compare thecalculated angle to a threshold angle value. The system may be furtherconfigured to update the probabilities of the instrument advancing intoeach of the child segments in response to the calculated angle beinggreater than the threshold angle value. Thus, in certainimplementations, the system may not update the probabilities of theinstrument advancing into each of the child segments unless theorientation of the instrument forms an angle with the initialorientation that is greater than the threshold angle value. The method400 ends at block 425.

The above-described method 400 may be particularly advantageous whenperformed using unregistered location data. That is, since unregisteredlocation data may not provide an exact mapping of the location and/ororientation of the instrument to the model coordinate system, the systemmay use the relative orientation of the instrument at two or moresuccessive times to determine whether a change in the measuredorientation of the instrument is consistent with the instrument beingarticulated towards one of the child segments. The detected change inthe measured orientation of the instrument (e.g., within the locationsensor coordinate system) may be consistent with a change in thephysical orientation of the instrument to be pointed in a direction thatis closer to the physical orientation of one of the child segments. Inother words, the relative change in the orientation of the instrument inthe location sensor coordinate system from the initial orientation tothe subsequent orientation may still be indicative of a change in thedirection of advancement of the instrument towards one of the childsegments when determined using unregistered location data. Thus, sincethe location sensor coordinate system may not be registered to the modelcoordinate system during an initial phase of certain procedures, themethod 400 can provide branch prediction during the initial phase, whichcan be used by a sensor fusion technique (e.g., localization system 90of FIG. 15 ).

In one example, when the data indicative of a difference between theinitial orientation and the subsequent orientation is consistent withthe instrument being advanced or directed towards the patient's leftprimary bronchi (e.g., the second generation airway 320), the system maypredict that the instrument will be advanced into the left primarybronchi rather than the right primary bronchi. Accordingly, the systemmay update the prediction for each child branch based on whether thechange in the orientation is indicative of the instrument being advancedor directed towards or away from the corresponding child branch.

B.5. Selection of the Initial Location

As described above, the method 400 may include determining a firstorientation of the instrument at block 405 when the instrument islocated at an initial location. The location sensor-based branchprediction system may select the initial location of the instrument inresponse to initialization of the state estimation module 240. Incertain embodiments, the location sensor-based branch prediction systemmay select the first indication of the location of the instrumentproduced by the state estimation module 240 after initialization as theinitial location. However, in other embodiments, the system may selectother locations (e.g., the second, third, etc. location of theinstrument) produced by the state estimation module 240 as the initiallocation.

In some embodiments, the system may be configured to select the initiallocation of the instrument based on a determination that the orientationof the instrument at the initial location is substantially aligned withthe orientation of the current segment (e.g., aligned with alongitudinal axis of the current segment). As used herein, the systemmay consider the orientation of the instrument to be substantiallyaligned with the orientation of the current segment when the differencebetween the orientations of the instrument and the current segment isless than a threshold difference. However, since the received locationsensor data may be unregistered to the model coordinate system, thesystem may not be able to directly compare the orientation of theinstrument to the orientation of the current segment.

In one implementation, the system may be configured to receive anindication from a user that the instrument is aligned with the currentsegment. The user may be able to confirm that orientation of theinstrument is currently aligned with a first generation segment based onother sensors of the system (e.g., a camera located at the distal end ofthe instrument). In one implementation, the system may provideinstructions to the user to drive the instrument to a defined positionwithin the luminal network (e.g., the carina 310 of FIG. 18 ) and toretract the instrument by at least a defined distance from the definedposition. The system may determine the location of the distal end of theinstrument at the position before or after retraction, and set theorientation of the instrument at this point as the initial orientationwhich is substantially aligned with the orientation of the currentsegment.

In other implementations, the system may be configured or programmed toautomatically select the initial location during the driving of theinstrument without receiving user input. In one embodiment, the systemmay track the orientation of the instrument as the instrument isadvanced through the current segment over a period of time and, inresponse to the orientation of the instrument being substantiallyunchanged for a threshold time period, the system may determine that theorientation of the instrument during the identified period is alignedwith the orientation of the current segment. In some embodiments, thesystem may determine that the orientation of the instrument issubstantially unchanged when a maximum difference between the measuredorientations of the instrument over the time period is less than athreshold difference.

B.6. Confirmation of a Registration Process

In certain implementations, the system may be configured to perform aregistration process in order to register the coordinate system of thelocation sensor(s) to the coordinate system of the model of the luminalnetwork. The registration may be stored in a registration data store 225as shown in FIG. 17 . The registration process may include a process tofacilitate the use of unregistered location data to determine theregistration between the location sensor coordinate system and the modelcoordinate system. In certain embodiments, the registration process maybe performed based on maintaining a history of the data received fromthe location sensor(s) and matching the shape formed by the locationdata history to the candidate paths along which the instrument cantravel based on the model of the anatomy. The system may provideinstructions to the user to drive the instrument along a predeterminedregistration path and track the position of the instrument based on thelocation sensors in response to the user driving the instrument alongthe registration path during the registration process. For certainprocedures, the registration path may comprise a contra-lateralregistration path defining the shape of the path along which theinstrument is to be driven for the registration process. Thecontra-lateral registration path may include driving the instrument downa segment on a contra-lateral side of the luminal network with respectto the location of the target, retracting the instrument from thecontra-lateral side, and driving the instrument along the lateral sideof the luminal network along the path to the target (also referred to asa target path). Thus, the contra-lateral registration path may includedriving the instrument along a contra-lateral branch of the luminalnetwork outside the target path (e.g., along a branch that is on acontra-lateral side of the luminal network with respect to the target),returning the instrument back to the target path, and driving theinstrument along a lateral branch which is located along a part of thetarget path.

In certain procedures, the target may include a nodule (or lesion) towhich the instrument may be driven to facilitate diagnosis and/ortreatment. Thus, the memory may store the target path to the targetwithin the model and the contra-lateral registration path. During theregistration process, the system may be configured to confirm whetherthe user is currently driving the instrument into the correct branch ofthe luminal network as defined by the contra-lateral registration path,based on the predictions of whether the instrument will advance downeach of the child segments. Thus, the system may determine whether theinstrument is located along the contra-lateral registration path basedon the predictions. In one implementation, when approaching abifurcation in the luminal network (e.g., the bifurcation near theprimary bronchi), the system may compare the probability that theinstrument will advance into the branch along the contra-lateralregistration path to a threshold probability. When the probability isless than the threshold probability, the system may display anindication to the user that the user may not be driving towards thecorrect branch.

In another implementation, the system may determine that the instrumentwas advanced along the target path prior to being advanced down thecontra-lateral registration path, which may be indicative of the userinadvertently driving the instrument along a path that does notcorrespond to the contra-lateral registration path used during theregistration process. Since the contra-lateral registration path mayrequire the instrument being driven down the contra-lateral path beforebeing driven down the target path, the system may provide an indicationthat contra-lateral registration was unsuccessful in response todetermining that the instrument was advanced along the target path priorto being advanced down the contra-lateral registration path.

The system may also be configured to display an indication of thelocation of the instrument with respect to the model during a givenprocedure to provide feedback to the user. Accordingly, the system maydetermine a position of the distal end of the instrument with respect tothe model based on a plurality of sources of data indicative of thelocation of the instrument. In certain implementations, the system maydetermine the position of the distal end of the instrument based on oneor more of: the location data received from the location sensors, a setof commands provided to control movement of the instrument, andprediction(s) that the instrument will advance into the child segmentsof the current segment. Thus, the prediction(s) determined via themethod 400 of FIG. 19 may be one source of data that can be used by thesystem in localization and/or navigation of the instrument.

C. Registered Location Sensor-Based Branch Prediction

After the system has performed a registration process, registering thelocation sensor coordinate system to the model coordinate system, thesystem may be able to use the data generated by the location sensor(s)to determine an indication of the location of the distal end of theinstrument with reference to the model associated with the modelcoordinate system. Using the registered data location data, the systemcan produce a predication as to the child segment of the luminal networkinto which the instrument is most likely to be advanced. Depending onthe implementation, the use of unregistered location sensor data may notprovide sufficient accuracy for branch prediction in the luminal networkonce the instrument has been advanced into the luminal network beyond acertain distance. For example, once the instrument has been advancedinto the primary bronchi, the system may not be able to performed branchprediction without registered location sensor data. Accordingly, aspectsof this disclosure also relate to the use of registered location datafor branch prediction.

FIG. 20 illustrates an example skeleton-based model of a portion of aluminal network. In particular, the skeleton-based model 500 illustratesan implementation in which the luminal network is modelled as a“skeleton” 505 which may comprise a plurality of discrete segments, eachcorresponding to an individual lumen of the luminal network. The model500 illustrated in FIG. 20 may be used by a localization system 90 (seeFIG. 15 ) to calculate the position and orientation of the distal end ofan instrument. Each segment of the skeleton 505 may be assigned a unique“segment ID” and each of the segments forming the skeleton 505 may bedefined with respect to a center line of the corresponding lumen. Duringcertain medical procedures, the instrument may be navigated through theluminal network for at least a portion of the procedure. In particular,at a given point in time, the distal end of the instrument 525 may belocated within a current segment 510 of the model and the currentsegment 510 may branch or bifurcate into two child segments 515 and 520.As will be discussed in detail below, aspects of this disclosure relateto a branch prediction technique performed based on the orientation ofthe instrument when located within a current segment 510.

FIG. 21 is a flowchart illustrating an example method operable by arobotic system, or component(s) thereof, for registered locationsensor-based branch prediction in accordance with aspects of thisdisclosure. For example, the steps of method 600 illustrated in FIG. 21may be performed by processor(s) and/or other component(s) of a roboticsystem or associated system(s). For convenience, the method 600 isdescribed as performed by the location sensor-based branch predictionsystem, also is also referred to simply as the “system” in connectionwith the description of the method 600. The robotic system may includeat least one computer-readable memory having stored thereon a model of aluminal network of a patient.

At block 605, the location sensor-based branch prediction systemdetermines an orientation of an instrument with respect to the modelbased on location data generated by a set of one or more locationsensors for the instrument. In certain implementations, the locationdata includes registered location data received from the set of locationsensors. At the time the location sensor data is received by the system,the location sensors may be registered to a model of the luminalnetwork. Using the registered location data, the system may be able todetermine the location of the distal end of the instrument with respectto the model based on the location data. In certain implementations, thesystem may employ a sensor fusion technique (e.g., by using localizationsystem 90 of FIG. 15 ) which determines the location of the distal endof the instrument using multiple sources of data. Since the receivedlocation data is registered to the model, the system may be able to makea measurement of the location of the instrument with respect to themodel.

In certain implementations, a distal end of the instrument may belocated within a first segment of the model when the system determinesthe orientation of the instrument at block 605. In one embodiment, thedistal end of the instrument 525 is located within a current segment 525which includes two child branches 515 and 520 as shown in FIG. 20 .

At block 610, the location sensor-based branch prediction systemdetermines an orientation of a first one of the child segments. In theexample of FIG. 20 , the orientation of each of the child segments 515and 520 may be determined based on the orientation of the correspondingsegment of the skeleton 505 with respect to the model coordinate system.Depending on the implementation, the current segment may correspond toany one of the segments of the skeleton 505 which includes two or morechild segments.

At block 615, the location sensor-based branch prediction determines aprediction that the instrument will advance into the first child segmentbased on the orientation of the instrument and the orientation of thefirst child segment. This may include, for example, the systemdetermining the difference between the orientation of the distal end ofthe instrument 525 and the orientations of each of the child segments515 and 520 in the FIG. 20 embodiment. The model may include dataindicative of the orientations of each of the segments. In certainembodiments, the data indicative of the orientations of the segments maybe included in the data representing the skeleton 505, in which thelocation, length, and orientation of each segment of the skeleton 505may be defined in memory. Thus, the system may determine the orientationof the instrument in the model coordinate system using the registeredlocation sensor data and compare the orientation of the instrument tothe orientations of each of the child segments. The method ends at block620.

Depending on the embodiment, the prediction may include: anidentification of the child segment the instrument is most likely to beadvanced, a probability to each of the child segments that theinstrument will be advanced into the corresponding child segment, anordered list of the child segments from the highest probability to thelowest probability, etc. Thus, in certain embodiments, the predictionmay include data indicative of a probability that the instrument willadvance into each of the child segments. In some implementations, thesystem may assign higher probabilities to child segments having a lowerdifference in orientation from the orientation of the instrument. Thus,the system may determine data indicative of a difference between theorientation of the instrument and the orientation of each of the childsegments to aid in determining the corresponding probabilities.

In certain implementations, the system may further determine an anglebetween the orientation of the instrument and the orientation of each ofthe child segment. The angle between the orientation of the instrumentand a given child segment may be indicative of the difference betweenthe orientations. Thus, the system may be configured to assign aprobability to a given child segment that is inversely proportional tothe angle between the given child segment and the instrument. In oneimplementation, the system may determine the angle based on the dotproduct between the orientation of the instrument and the orientation ofa given child segment. Since a smaller angle may be indicative greateralignment between the orientation of the instrument and a given childsegment, the system may also use the inverse of the determined angle tocalculate the probability for the given child segment.

The prediction may be used by the system as a source of data for afusion technique (such as, e.g., the localization system 90 of FIG. 15 )used to determine the location of the distal end of the instrument withrespect to the model. In certain implementations, the system maydetermine the position of the distal end of the instrument based on oneor more of: the location data received from the location sensor(s), aset of commands provided to control movement of the instrument, and theprediction(s) that the instrument will advance into each of the childsegments.

In certain implementations, the system may apply an auxiliary techniquefor branch prediction in addition to the above-describedorientation-based prediction. In one implementation, the auxiliarytechnique may include a location-based prediction which compares thelocation of the distal end of the instrument to the location of thebeginning of each of the child segments. The system may determine anauxiliary prediction that the instrument will advance into each of thechild segments based on the location of the distal end of theinstrument. The prediction may be based on the location of the distalend of the instrument with respect to each of the child segments.Further details and examples of location-based prediction techniqueswhich can be used as the auxiliary technique are described in U.S.Patent Publication No. 2017/0084027, referenced above. In oneimplementation, when the location senor data is indicative of the distalend of the instrument as being located closer to one child segment thananother child segment, the system may assign a higher probability to thecloser child segment than the farther child segment. For example,referring back to FIG. 20 , when the distal end of the instrument 525 iscloser to a first one of the child segments 515 than a second one of thechild segments 520, the system may assign a higher probability to thefirst child segment 515. In some implementations, using the fusiontechnique, the system may determine the location of the distal end ofthe instrument based on both the orientation-based prediction techniqueand the auxiliary location-based prediction technique. The system mayweight the results of each of the branch prediction techniques based onvarious factors, including the accuracies associated with each of thetechniques, an error associated with a particular prediction, etc.

One advantage associated with orientation-based location sensor branchprediction over location-based branch prediction is that theorientation-based prediction may be performed continuously during thedriving of the instrument through the luminal network. For example, alocation-based branch prediction technique may not provide an accurateprediction unless the distal end of the instrument is within a thresholddistance of the bifurcation of the current segment into the childsegments. Since the location-based prediction technique relies onlocation sensor data, the location-based prediction technique may besusceptible to errors in the location sensor registration as well as tojitter in the location sensor data. Thus, when the instrument isrelatively far away from the child segments, the distance between theinstrument and each of the child segments may not be indicative of thechild segment to which the user is driving the instrument towards. Incontrast, the orientation of the instrument may be more stronglycorrelated with the child segment into which the user is driving theinstrument even when the instrument is relatively far away from thebifurcation defined by the child segments. Accordingly, in someimplementations, the orientation-based branch predication techniquesdescribed herein may be applied as the distal end of the instrument isadvanced from a beginning of a current segment to an end of the currentsegment. In other embodiments, the system may apply theorientation-based branch prediction technique independent of theposition of the distal end of the instrument within the current segment.

3. Implementing Systems and Terminology.

Implementations disclosed herein provide systems, methods andapparatuses for location sensor-based branch prediction.

It should be noted that the terms “couple,” “coupling,” “coupled” orother variations of the word couple as used herein may indicate eitheran indirect connection or a direct connection. For example, if a firstcomponent is “coupled” to a second component, the first component may beeither indirectly connected to the second component via anothercomponent or directly connected to the second component.

The functions described herein may be stored as one or more instructionson a processor-readable or computer-readable medium. The term“computer-readable medium” refers to any available medium that can beaccessed by a computer or processor. By way of example, and notlimitation, such a medium may comprise random access memory (RAM),read-only memory (ROM), electrically erasable programmable read-onlymemory (EEPROM), flash memory, compact disc read-only memory (CD-ROM) orother optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. It should be noted that a computer-readablemedium may be tangible and non-transitory. As used herein, the term“code” may refer to software, instructions, code or data that is/areexecutable by a computing device or processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isrequired for proper operation of the method that is being described, theorder and/or use of specific steps and/or actions may be modifiedwithout departing from the scope of the claims.

As used herein, the term “plurality” denotes two or more. For example, aplurality of components indicates two or more components. The term“determining” encompasses a wide variety of actions and, therefore,“determining” can include calculating, computing, processing, deriving,investigating, looking up (e.g., looking up in a table, a database oranother data structure), ascertaining and the like. Also, “determining”can include receiving (e.g., receiving information), accessing (e.g.,accessing data in a memory) and the like. Also, “determining” caninclude resolving, selecting, choosing, establishing and the like.

The phrase “based on” does not mean “based only on,” unless expresslyspecified otherwise. In other words, the phrase “based on” describesboth “based only on” and “based at least on.”

The previous description of the disclosed implementations is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these implementations will bereadily apparent to those skilled in the art, and the generic principlesdefined herein may be applied to other implementations without departingfrom the scope of the invention. For example, it will be appreciatedthat one of ordinary skill in the art will be able to employ a numbercorresponding alternative and equivalent structural details, such asequivalent ways of fastening, mounting, coupling, or engaging toolcomponents, equivalent mechanisms for producing particular actuationmotions, and equivalent mechanisms for delivering electrical energy.Thus, the present invention is not intended to be limited to theimplementations shown herein but is to be accorded the widest scopeconsistent with the principles and novel features disclosed herein.

What is claimed is:
 1. A system, comprising: a processor; and at leastone computer-readable memory in communication with the processor andhaving stored thereon a model of a luminal network, the model associatedwith a model coordinate system, the memory further having stored thereoncomputer-executable instructions to cause the processor to: determine anorientation of an instrument based on location data generated by a setof one or more location sensors for the instrument, a distal end of theinstrument being located within a parent segment of the model and theparent segment branching into at least a first child segment and asecond child segment, the location data being unregistered to the modelcoordinate system; determine an angle formed between orientations of thefirst child segment and the second child segment; and determine, basedon the angle and the orientation of the instrument, a prediction thatthe instrument will advance into the first child segment, wherein thelocation data remain unregistered to the model coordinate system untilafter the prediction has been determined.